Determination of the remaining service life of machinery and equipment based on probabilistic models
© Leifer L.A., Kashnikova P.M., 2007
CJSC "Privolzhsky Center"
financial consulting and assessment"
Determining the residual service life and residual resource is an important element in the procedure for assessing the market value of machinery and equipment.
Within the cost approach, the residual service life (residual resource) is necessary to determine the residual value and, accordingly, the replacement cost of the object. When implementing the income approach, the residual period determines the period during which cash flows should be expected, and therefore its value significantly affects the estimated value of the market value. With a comparative approach, the remaining service life serves as the basis for adjusting the prices of analogues that differ from the object being valued in terms of the amount of operating time they have worked. Therefore, the accuracy of estimating the market value of machinery and equipment largely depends on how correctly the residual service life (residual resource) of the valued object is determined. Depending on what information the appraiser has, different methods for determining the remaining service life and residual resource are possible. The most reliable forecast of the remaining life can be carried out if a full-scale technical diagnosis of the machine is performed using appropriate diagnostic tools and introscopy. This approach is costly, and therefore, with the exception of cases where single and expensive machines or technological lines are being assessed, it is not usually used in widespread appraisal practice. Methods for individual prediction of the residual life of machines and structures, based on models of physical processes of wear of machines and structures (accumulation of fatigue damage, wear of mechanisms, etc.), set out in various publications (see, for example,), have also not found practical application when estimating the cost of machines due to their labor intensity and the need to use the complex mathematical apparatus of the theory of random processes.
The problem of estimating the cost of large amounts of equipment and machinery has led to the need to create simplified technologies that provide “flow” assessment, using a minimum of input information about the object of assessment. These requirements are also met by technologies for determining the remaining service life, based on linear or exponential wear models.
We will not consider the advantages and disadvantages of these methods. Let us only note that they are fundamentally based on deterministic wear models. In this case, the residual service life (resource) within these models is usually defined as the difference between a certain standard service life and its effective age.
In recent years, a different approach has begun to be used in the practice of assessing machines and equipment, based on the methodology developed within the framework of the theory of reliability of machines and complex structures. In contrast to deterministic wear models, this methodology is based on the idea that the remaining service life (resource) of a machine is a random variable that can only be described by probabilistic models. This methodology expands the capabilities of assessment methods and makes them more consistent with physical wear processes and common sense. Within the framework of this methodology, it is possible to understand and take into account when calculating the cost of an object the fact that the actual service life may significantly exceed the standard one. In this case, the service life (resource) established in the documentation has the meaning of the minimum service life (resource), during which the manufacturer guarantees normal operation with a high probability.
In this article, a statistical approach to the problem of predicting the remaining service life (resource) is developed on the basis of models that, according to the authors, may be the most appropriate in many real situations related to the evaluation of machines in conditions where the loss of value is mainly due to the physical degradation of the object assessments. Basic concepts, terms and definitions
Since problems related to the analysis of service life and resource of technical devices and structures (hereinafter referred to as objects) are studied within the framework of reliability methodology, the terms and definitions used in the article are taken mainly from the well-known standard.
Limit state - a state of an object in which its further operation is unacceptable or impractical, or restoring its operational state is impossible or impractical.
Notes:
1. An object can go into a limit state while remaining operational if, for example, its further use for its intended purpose becomes unacceptable according to the requirements of safety, economy and efficiency.
2. Reaching the limit state is not limited to physical wear and tear. As can be seen from the definition, the transition to a limit state can also be caused by the influence of factors of functional obsolescence.
3. Usually, when a limiting state is reached, the object is decommissioned. This, however, does not mean that the value of an object that has reached its limit state is zero. As an analysis of the literature has shown (and this has been confirmed by our research), the cost of an object that has reached its limit state is usually 10–20% of the initial cost. This cost may include the cost of remaining parts, materials, etc.
The service life of an object is a calendar time equal to the period of operation, counted from the commissioning of the object until reaching the limit state (decommissioning).
The resource of an object is the total operating time of an object, expressed in hours, kilometers, etc., counted from the commissioning of the object until reaching the limit state (decommissioning).
Notes:
1. During standard operation, usually the operating time, measured in hours or kilometers (for vehicles), is proportionally related to the service life. Therefore, in the future we do not make a distinction between these concepts and will use one of these terms, understanding that all formulas, reasoning and conclusions related to one of them apply to the same extent to the other.
2. The actual moments when objects reach their limit state can vary significantly depending on the individual properties and operating conditions of the objects. Therefore, the service life, as well as the resource of the object, should be considered random variables. They can only be described by probabilistic models. The distribution density or distribution law is usually used as such a model. In economic methodology, a similar concept is used: “survival curve”. More details about probabilistic models in the next chapter.
Average service life (Average resource) – The average value of a random variable - service life (resource), counted from the commissioning of an object until reaching the limit state (decommissioning).
Established (Standard) service life (established resource) - the service life established in the technical documentation.
Notes:
1. The established (Standard) service life characterizes the durability of an object, its ability to maintain operational characteristics for a specified period. Removal of an object from operation due to reaching a limit state before the end of the established service life is considered unlikely. At the same time, the achievement of the standard period by an object does not mean that the object has reached its limit state and must be decommissioned. To ensure reliable operation of an object within a specified period, the object must have a certain margin of safety, which makes it possible to confidently operate the object during the standard period and for some time after the end of this period. The development and testing of the object carried out at the manufacturing plant are aimed at ensuring reliable operation for a specified period (specified resource) and at ensuring this reserve. From a probabilistic point of view, the term specified in the documentation represents a quantile of the expected service life distribution.
2. It is necessary to distinguish between the average service life and the standard service life. The standard service life is not the average service life, but it can be used as initial information to determine the average service life and other statistical parameters characterizing the durability of an object.
3. If the design or operational documentation does not indicate the service life, then the standard period may be a value calculated on the basis of the depreciation rate of an object of this class. In essence, this value also characterizes the durability of an object.
The age of an object is the period of time from the start date of operation to the current moment.
Remaining service life - Calendar duration of operation from the current moment until it reaches the limit state. It differs from the service life in that the current moment, before which it has already been in operation for some time, is taken as the starting point, and not the beginning of operation.
The residual resource of an object is the operating time of the object, expressed in hours, kilometers, etc., from the current moment until it reaches its limit state. It differs from the resource of an object in that the current moment is taken as the starting point, before which it has already been in use for some time, and part of the initial resource has been exhausted.
Notes:
1. Individual characteristics of an object (remaining service life and residual resource) are random variables and can be accurately determined only after its limiting state has occurred. Until these events occur, we can only talk about predicting these values with greater or lesser probability. Therefore, the remaining service life is the predicted value of the expected time, after which the object will reach its limit state and will be decommissioned. It should be especially emphasized that the remaining period in the general case is not equal to the remaining time before reaching the standard period. The same applies to the residual resource.
2. Since the remaining service life (residual resource) is a random variable, it can only be described by probabilistic models. As such a model, just as in the case of the initial service life (resource), a survival curve can be used.
Average residual service life (Average residual resource) - the average value of a random variable - the residual service life (resource), counted from the current moment until the limit state is reached (decommissioning).
Notes:
1. It should be clearly understood that the average remaining service life does not indicate the exact period of time that the assessed object will be in operation. It characterizes a certain center of dispersion of moments of time, around which (some earlier, some later) objects of a given class that have reached the limit state will be decommissioned. Since at the time of assessment it is not possible to determine the exact time that an asset will still be able to operate, the average remaining life represents the best guide to the expected service life of the asset being assessed.
2. The average remaining service life depends on the initial durability characteristics of the object and its age. The older the object, the shorter its average remaining life. Thus, the average remaining life decreases as the age of the subject property increases. However, achieving the standard life does not mean that the average remaining service life is zero.
Probabilistic models for describing service life (resource)
Since service life is a random variable, probabilistic models should be used to describe it. The probability that the object will not reach the limit state over time is determined as P(J) = P (t ³ J)
The function P(J) shows how many objects on average will “survive” until time t. That's why it's called the "survival curve." The survival curve defined in this way is related to the probability distribution function F(J) by the relation: F(J) = 1- P(J)
The distribution density of time before the onset of the limit state f(J) is the derivative of the distribution function: f(J) = dF(J)/dJ = - dP(J)/dJ
Moreover, if time is counted from the current moment t, which characterizes the time until which the object was already in operation, then P(J /t) characterizes the probability distribution of a random variable - the remaining service life. In the language of probability theory, P(J /t) is the conditional probability that the residual service life will be no less, provided that the object functioned properly until the current moment - t. It is necessary to distinguish between a theoretical probability distribution and an empirical one (or sample, i.e., constructed from sample data). Constructing an empirical distribution based on statistical data does not present any fundamental difficulties. However, in order for the empirical distribution to be directly used to establish the theoretical distribution, large amounts of data are needed. Therefore, all conclusions regarding the theoretical distribution are made based on an analysis of the nature of the data, the nature of the processes leading to the limit state and the limited volume of sample data.
In the literature on assessing the market value of real estate, machinery and equipment, when discussing issues related to determining the residual service life, a term borrowed from the theory of actuarial calculations has become widespread [see, for example, 8, 16] - “survivor curve” . A survival curve is a graph showing the number of units from a given group of assets that remain operating at some point in time over the forecast interval. In other words, it characterizes the process of decommissioning objects as they reach a limiting state. This curve is a statistical analogue of the probability P(J) introduced above. In what follows, by the survival curve we will understand the theoretical and empirical (statistical) version of the function P(J).
Various distribution laws are used to describe the survival curve. The most commonly used tools for this purpose are the so-called Iowa-type survival curves. They were developed as a result of a study of empirical data relating to the characteristics of all types of machines and equipment that have remained operational. Subsequently, they were used to assess the remaining useful life of the property of trade and utility enterprises, electricity, water and gas supply, railways, etc. In relation to the valuation of machines in Russian valuation practice, similar models were considered in the works of V. N. Trishin). It should be especially noted that in these works the proposed methods are brought to specific solutions and, what is especially important, the software system that implements these methods is based on input data available to the practicing Appraiser. In addition, probabilistic models for describing the useful life are used in problems of assessing the value of intellectual property objects. In the cited work, well-known probability distributions are used to describe the useful life, in particular, the Weibull model and Iowa-type survival models. Along with the models proposed in the State of Iowa, for the probabilistic description of the service life of machines, mechanisms, and complex technical systems, the lognormal distribution can also be used, which, along with the Weibull distribution, has been widely used and developed in the theory of reliability of technical systems, machines and complex structures.
The choice of one or another distribution is determined by the nature of the prevailing physical processes, the availability of initial information and the capabilities of computational procedures.
For the practical use of probabilistic models for the purpose of estimating market value, two main questions are:
1. How, based on the available information, can we determine the parameters of the survival curve (parameters of the service life distribution - random time until the limit state is reached)? 2. How to determine the characteristics of the residual service life if the age of the object and the parameters of the distribution of time before reaching the limit state (survival curve) are known?
This article proposes a model that allows, under the accepted assumptions, to answer these questions and thereby create real prerequisites for the practical use of probabilistic models in problems of determining the remaining service life of machines and equipment. The lognormal distribution is used as such a model, which, according to the authors, is most adequate to the processes of physical wear, fatigue accumulation of damage and other mechanisms of loss of performance of machines and mechanisms.
The lognormal distribution can be derived as a statistical model for a random variable whose values are obtained by multiplying a large number of random factors. The lognormal distribution is used in a variety of fields, from economics to biology, to describe processes in which an observed value is a random fraction of a previous value. The rationale for the applicability of the lognormal distribution to describe service life is also based on the effect multiplication property inherent in this distribution. Therefore, this distribution has been widely used and developed in works on the analysis of degradation processes of mechanical systems.
Let us denote the dimensionless time equal to the ratio of the service life (t) to the standard service life (t x) by the letter t: t= t /t x
Then, in accordance with the adopted service life model, the distribution density of the random variable (t) has the form:
The distribution density contains all the information regarding the service life. However, to directly carry out the assessment, it is necessary to know the main characteristics of a given distribution (m and s). 
Rice. 1. Distribution density of a random variable (t)
The expected value (T), dispersion (D) and coefficient of variation (r) of the random variable t (service life, specified in dimensionless form) are determined through the distribution parameters (m and s) as follows: 
(1)
(2)
(3)
From standard service life to actual service life distribution parameters
It is usually not possible to carry out durability tests on objects similar to the object being assessed during the assessment process. Therefore, to determine the distribution parameters, you should use the information available to the evaluator. Such information can be used as general information regarding the object of assessment and the standard service life specified in the operational documentation. As noted above, if there is no data on the service life, you can use depreciation rates, which also provide information about the object being evaluated.
Let us analyze the relevant information that allows us to determine the main characteristics of the lognormal distribution.
An analysis of the literature, summarizing numerous studies on the reliability and durability of machines and equipment, shows that the coefficient of variation for machines and equipment lies in the range: 0.3 – 0.4. This information allows you to determine the distribution parameter -D. In order for the standard service life related to a given object to be used to determine distribution parameters, we take into account that the standard service life is a calendar time during which the object must function properly (more precisely, it must not reach its limit state) . Essentially, the standard service life indicates the minimum time during which an object must be in operation if no abnormal situations occur. Thus, if we assume that an object with a high probability (for example, 0.9) should serve for a given period, then from the point of view of the adopted model, the standard period represents a 10 percent quantile of the distribution. Using the above information and the corresponding assumptions, it is easy to calculate the parameters of the lognormal distribution and construct a survival curve characterizing the process of disposal of the assessed objects during the period of operation.
Let us set the level a, it will represent the probability that the object of assessment will reach the limit state before the expiration of the standard period, which in turn is determined by the integral 
(4)
Using this equation (4) and relations (1), (2) and (3), it is possible to calculate the values of the dimensionless average service life (T) for given values of a and r. Let us recall that the dimensionless average service life (T) is a value equal to the ratio of the average value of the actual service life to the standard service life.
Table 1 presents the results of such calculations for various values of a and r.
Table 1. Values of dimensionless average service life (T)
It is also possible to calculate the parameters of the lognormal distribution, which characterizes the probabilistic properties of the process of decommissioning assessment objects from service. In Fig. Figures 2 and 3 show, respectively, the distribution density of the service life of machines, equipment and structures and the survival curve (sometimes called the mortality curve), which describes the process of decommissioning of objects. 
Rice. 2. Service life distribution density (r =0.3, a =0.1) 
Rice. 3. Survival curve (r =0.3, a =0.1)
In this case, the distribution density and survival curve are constructed based on the conditions: r =0.3, a =0.1. The basis for choosing such initial data was two circumstances:
1. The limit state of mechanical systems occurs mainly due to the processes of physical wear and fatigue accumulation of damage. Therefore, based on numerous studies in reliability theory (see, for example,), a value equal to 0.3 – 0.4 can be taken as the coefficient of variation.
2. The standard period (assigned), specified in the design or operational documentation, is nothing more than the minimum permissible service life of the object, during which the object should not reach its limit state. Since, however, such a possibility cannot be completely excluded, we assume that an object is decommissioned and written off in no more than 10% of cases. As a result, the survival curve mainly characterizes the process of disposal of objects in the period of time after the standard service life. Naturally, in accordance with this assumption, the average service life of the object, which is used in further assessment calculations, exceeds the standard service life, which is quite justified from the point of view of the real picture of the market.
Remaining service life.
If an object has reached a certain age, then it is natural to expect that its remaining service life will decrease somewhat. Moreover, the higher the age of the object (assuming the same life history of the objects), the shorter its residual life. This statement corresponds to all known models of loss of value and common sense.
In this case, the distribution of the residual service life of the assessed object and, accordingly, the survival curve, which characterizes the probabilistic process of disposal of objects of a given class that have survived to a given age, can be calculated based on the conditional probability distribution. The conditional density of the lognormal distribution of the residual service life, expressed in relative units, corresponding to the condition that the object has survived to age t, is determined as follows: 
(5)
Further calculations and corresponding graphs are constructed under the assumption that the coefficient of variation r = 0.3 and the permissible level of retirement of objects from operation before they reach the standard period a = 0.1 
Rice. 4. Conditional distribution density of the residual service life, provided that the object was in operation until the current moment.
Note that n is the age of the object at the time of assessment in relative units, numerically equal to the actual operating time divided by the standard service life:
n = t / t n
Knowing the distribution density of the residual service life (5), it is possible to determine the average value of the residual service life T (in relative units) provided that the object has already been in operation for some time (t). Below is the dependence of the average remaining service life on the actual service life preceding the assessment date. This relationship is constructed by statistically modeling the random variables generated by said distribution density and then calculating the mean and median. The results obtained reflect the probabilistic nature of machine durability and are more consistent with reality than deterministic models. In particular, they take into account that the achievement of the target deadline by an object does not mean that the resource is completely exhausted. With the parameters included in the above calculations, an object that has exhausted its standard life retains the possibility of further operation on average for up to 40% of the standard life. The remaining period takes into account the built-in reserve for the resource of the machine, since the standard period is not the period of complete exhaustion of the resource. The graph also shows that with an increase in the previous service life, the average value of the residual service life decreases, and an object that has worked significantly longer than its standard service life expects to soon reach its limit state.
The examples below show how the stated theory can be used in practical calculations in the process of assessing the market value of machinery and equipment.
Rice. 5. Dependence of the average value of the remaining life (T) on the previous service life (n).
Examples of calculating the remaining life of movable property.
In conclusion, we provide examples of determining the average residual life, illustrating the process of assessing the residual service life when assessing machinery and equipment using a graph for the average value of the residual life (Fig. 5).
Example 1.
1. The object of assessment is a complex production line with a given standard service life of 20 years.
2. The equipment was purchased from dealers and put into operation 14 years ago. The line was operated under normal conditions in compliance with all requirements of operational documentation (scheduled preventive maintenance, preventive maintenance, etc.) Currently, it is in working condition.
3. Degradation processes occurred under the influence of physical wear and fatigue accumulation of damage. The coefficient of variation can therefore be taken equal to 0.3.
4. Determining the average remaining useful life is required to establish the period over which the asset can be expected to generate cash flows. This value is required to implement the income approach.
Calculation
The following are used as initial data:
standard period – 20 years,
current age is 14 years (in relative units 14/20 = 0.7).
From the graph we determine the average residual service life in relative units, which will be 0.6.
Hence the average remaining period is 0.6 * 20 = 12 years.
Example 2.
1. The object of assessment is an agricultural tractor, the standard service life according to the design documentation is 12 years
2. The tractor was purchased from a retail chain and was operated normally for a full service life of 12 years.
3. At the moment, the tractor is operational, that is, capable of performing specified functions in accordance with the requirements of regulatory, technical and design documentation. Resource parameters are within acceptable limits.
5. Determination of the residual service life is required to determine the amount of loss in value of an object that has served its full service life and has not reached its limit state, within the framework of the cost approach.
Calculation
Initial data:
standard period – 12 years,
current age is 12 years (in relative units 12/12 = 1).
From the graph we determine the average remaining service life in relative units: 0.4.
Thus, the average remaining term is: 0.4 * 12 = 4.8 years.
From here, if we consider the amount of wear and tear using the economic life method, we get: Wear = current age/current age + average residual life. Wear = 12/ (12+4.8) = 0.7. Using the obtained depreciation value as initial data, you can calculate the current value of the object.
Example 3.
1. The object of evaluation is an imported passenger car manufactured in 1993, purchased on the secondary market. Currently the car is 11 years old.
2. The operational documentation does not contain a standard service life. However, some idea of it is given by depreciation rates that reflect the average service life of objects of this class. Based on depreciation standards, the standard service life of a car of this class is 7 years.
3. At the moment, the car is operational, i.e. capable of performing specified functions in accordance with the requirements of regulatory, technical and design documentation. Resource parameters are within acceptable limits.
4. Degradation processes related to resource parameters (gaps in joints, wear in bearings, gears, shafts, etc.) occurred mainly under the influence of physical wear. Therefore, the coefficient of variation of service life can be taken equal to 0.3.
5. Despite the fact that the car has served its standard service life, since the car is in good condition, a decision was made to continue its operation. This should be reflected in the assessment of the market value of the enterprise's fixed assets. To do this, it is necessary to determine the remaining service life.
Calculation
We use as initial data:
standard period – 7 years,
current age is 11 years (in relative units 11/7 = 1.5). From the graph we determine the average remaining service life (in relative units): - 0.3
Thus, the average remaining term is 0.3 * 7 = 2.1 years.
Conclusions.
1. The article describes an approach that allows one to predict the remaining service life with a minimum of initial information. The initial data for predicting the average value of the remaining service life are: the standard service life of the object and the actual service life preceding the moment of assessment.
2. Implicitly, the presented method takes into account information about wear mechanisms. This information is contained in the value of the service life variation coefficient included in the calculation formulas. This increases the information content of the method, giving it additional advantages compared to the simplified model.
3. The approach outlined in the article is based on probabilistic models and develops methods for determining the statistical characteristics of the remaining service life, based on the use of survival curves, successfully used in actuarial calculations.
4. Fundamental to the proposed model is the recognition that the standard service life is not equal to the expected life span during which the object reaches its limit state. The method is based on the assumption that in the vast majority of cases (for example, no less than 90%), the object must operate successfully without reaching the limit state throughout the entire standard period.
5. The lognormal distribution is used as a basic probability model, which, together with the Weibull distribution and survival curves, called Iowa curves, allows us to describe the process of retirement of objects from service as they reach a limiting state.
6. Within the framework of the stated method, an individual analysis of the technical condition of the object being assessed is not assumed, which would certainly contribute to increasing the accuracy of the forecast of the residual service life (residual resource) of each specific object. Therefore, the presented method can be used for mass assessment of the cost of machines, when it is necessary to minimize the costs of assessing a large number of machines and equipment.
7. The description of the method and its interpretation relate to the evaluation of machinery and equipment. However, with minor clarifications, the method can be applied to determine the remaining service life of real estate, intellectual property and other objects of assessment, for which the service life or useful life can be considered a random variable.
Literature
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Let us recall that the useful life may be equal to or different from the standard service life, since the expected period of operation is influenced by various factors, including the conditions under which the fixed asset will be used, its obsolescence, etc. To calculate the useful life of fixed assets, you should use Appendix 3 to Instruction No. 37/18/6, in which all fixed assets are divided into eight enlarged groups, each of which has a certain range of terms. The lower and upper limits of this range are calculated by multiplying the standard service life by the coefficient given in the said annex. The useful life is established in years (the corresponding number of months) and cannot be less than a year.
Standard service life
Based on these data, tables are constructed: the cost of equipment being retired and the cost of equipment remaining in operation.
In this case, depreciation charges are not taken into account. To increase the objectivity of the assessment, it is best to bring all existing costs to one point in time.
Based on the data obtained in this way, retirement rates are calculated for one or several groups (units) of equipment, on the basis of which the amount of equipment wear is determined at different service life and a residual potential curve is constructed.
Within the framework of this technique, 18 typical curves are identified that can describe any equipment.
Curves are divided into “symmetrical”, i.e.
Problems of determining the average service life of equipment
But it gives a general idea of depreciation. Average service life This figure can be called quite subjective.Attention
It is used in cases where the owner does not have information about the exact service life of a particular equipment, but it is necessary to calculate depreciation for it.
For example, the documents indicate the value “from 10 to 15 years” or not indicated at all, but the figure for calculations must be justified.
In such cases, third-party sources are used. You can seek help from those who have already used such equipment and find out how many years it has served.
All that remains is to add up the values and divide by the quantity to get the average service life of the equipment.
Determination of standard service life and useful life
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Important
In fact, houses built over 100 years ago (such as castles or palaces) still have strong structures.
At the same time, modern buildings quickly become unsuitable for use, since builders often skimp on materials, and engineers initially design less durable structures.What to do if the regulatory period has expired? The period during which the building must function is specified during the design. If its service life has expired, it will only be able to perform its original function if it is completely rebuilt.
The same approach applies to some other objects.
For example, an enterprise has a fleet of specialized cars for transporting various goods.
In just 1-2 years, their standard service life will expire, and this is more than 50 thousand units.
How to determine the standard service life of equipment
It is also worth noting that often the warranty period is one of the advertising moves. After all, the longer a manufacturer is willing to be responsible for its products, the more trust it inspires. How to extend the service life? Under different conditions, exactly the same devices can have very different service times.
That is, intended use, proper care and timely repairs can significantly increase the actual service life.
But with the normative one everything is much more complicated. Over time, technologies change, new materials and production methods appear.
Logic dictates that houses that were built earlier should last less because they used more primitive designs. If this is so, then the standard service life of new buildings should increase over time.
How to find out the standard service life of equipment
The auxiliary equipment of turbine units includes: condenser, pumps (condensate, gas cooling, circulation, feed, turbo pumps), deaerators, reduction-cooling units and evaporators, oil facilities, ejectors, regenerative units with heaters, exciters, spike lines, current and voltage transformers, instruments, electrical equipment of a turbogenerator within the machine room.3 For compressor machines and equipment used in the extraction of quartz in an aggressive acidic environment, a coefficient of 0.6 is applied.4 For centrifugal pumps used in the extraction of mineral fertilizers, a coefficient of 0.6 is applied.5 For lines on supports made of treated wood, built before 1990
Determination of the standard service life of equipment
But a lower-level manager, for example, the head of a workshop in which a machine is planned to be replaced, may think differently.
As a rule, the assessment of its operation - Calculating the accounting rate of return allows the use of accounting methods rather than the method of calculating profit. To obtain data on net income for each profit value, it is necessary to subtract the amount of depreciation charges from the expected profit, as shown in table. 14.1. We believe that the initial investment goes towards the purchase of equipment.
The equipment has a 4-year life, so depreciation expense for the year is $5,000.
We believe that for the first year for each of the six projects the following net income was received. With the straight-line method, depreciation is accrued at fixed rates over the entire service life of the equipment.
How to calculate the standard service life of equipment
Retirement of equipment can occur for the following reasons: write-off according to useful life (depreciation standards); write-off due to moral and physical wear and tear; sale; transfer to another organization; liquidation in case of accidents, natural disasters 4.
EQUIPMENT REPAIR From the book System of maintenance and repair of power equipment: Handbook by the author Yashchura Alexander Ignatievich 4.
EQUIPMENT REPAIR 2.5. Equipment service life From the book System of maintenance and repair of general industrial equipment: Handbook by the author Yashchura Alexander Ignatievich 2.5. Equipment service life 2.5.1. Equipment service life is the calendar duration (years and months) of the period during which the use of the equipment is considered useful.2.5.2.
The useful lives of fixed assets are established by Resolution 29.
Share on the page Next chapter Fuel service vehicles From the book Vehicles of the Soviet Army 1946-1991 by Evgeniy Dmitrievich Kochnev Fuel service vehicles BPS-PD-50 is a gas pumping station mounted on the chassis of a regular GAZ-51 onboard vehicle of the first production. It was a development of the BPS-PD station with a centrifugal single-stage PD pump, created in the 1930s for installation on airfield service vehicles. From the book Vehicles of the Soviet Army 1946-1991 by Evgeniy Dmitrievich Kochnev Airfield service vehicles Back in the early 1950s, GAZ-51 vehicles served as the base for the first Soviet dual-use airfield service vehicles.
ZIL-130 without its own pumping system. Intended for short-term storage and transportation of drinking water and other liquid food products in regions with temperate Fuel service vehicles From the book Vehicles of the Soviet Army 1946-1991 author Evgeniy Dmitrievich Kochnev Fuel service vehicles Based on the Ural-4320, there were only a few basic types of fuel tankers and tankers, which were mainly a development of fuel service vehicles designed for installation on the Ural-375 chassis and often produced in parallel. SERVICE CHRONOLOGY AND BRIEF TTD OF NOVIK-TYPE DESTROYERS From the book Novik-class destroyers in the USSR Navy author Likhachev Pavel Vladimirovich DETAILS OF SERVICE AND FEATURES OF EACH DESTROYER From the book Destroyers of Project 56 author Pavlov Alexander Sergeevich DETAILS OF SERVICE AND FEATURES OF EACH DESTROYER QUIET (p.
In this case, there is a need for careful monitoring of the movement of equipment and its accounting, which makes it possible to determine the location of a particular unit, unit, installation, tool, and the nature of its use.
In the oil industry and especially in oil refining and petrochemistry, where production processes take place in conditions of high temperatures and aggressive environments, the use of chemical and other types of metal protection against corrosion is extremely important.
This makes it possible not only to extend the service life of equipment and reduce the cost of its reproduction, but also to increase the safety of production processes.
STANDARD SERVICE LIFE OF MACHINERY AND EQUIPMENT
(developed by the Ministry of Heavy and Transport Engineering)
Name of machines and equipment (by groups and types of fixed assets) | Cipher | Standard service life, years |
|
1. All-metal passenger cars: | |||
hard compartments | |||
hard open and interregional | |||
luggage | |||
restaurants | |||
postal | |||
special technical and power station cars | |||
passenger carriages with wooden body | |||
2. Covered freight cars: | |||
universal | |||
paper cars | |||
cattle cars | |||
carriages for cars | |||
wagons for apatite concentrate | |||
grain hopper cars | |||
cement hopper cars | |||
hopper cars for mineral fertilizers | |||
bunker type car for granular polymers | |||
platform for heavy cuttings and pig iron |
Notes:
* - for the transportation of aggressive mineral fertilizers, a coefficient of 0.4 is accepted
** - with a stainless steel boiler, a coefficient of 1.5 is applied
*** - dump cars used for transporting goods on the main tracks of the Ministry of Railways, service life - 22 years
SERVICE LIFE OF EQUIPMENT – the period from the beginning of operation of the equipment (the beginning of the depreciation period) until its complete physical wear and tear (the end of the depreciation period). Establishing an economically justified service life of equipment is an objective procedure that allows us to establish the most reliable depreciation rate. If the service life is overestimated, then the physical life begins before the equipment is transferred to the finished product. In case of underestimation of the service life, the cost of the equipment is transferred to the finished product even before the onset of complete physical wear and tear. The most common method for determining the economically justified service life (Tn) is that as the service life of the equipment increases, annual depreciation charges (Ari) are reduced, and the cost of maintaining the equipment in working condition (Z) increases. In this case
Тнi = Ari Зрi? min.
The economically justified service life is determined by the year in which the annual total, i.e. annual depreciation charges plus annual costs for equipment repairs are minimal. Use of equipment beyond its standard service life should be taxed at the rate of depreciation of the last year of service.
Concise Dictionary of Economists. - M.: Infra-M. N. L. Zaitsev. 2007.
See what “EQUIPMENT SERVICE LIFE” is in other dictionaries:
guaranteed service life (of equipment)- - [A.S. Goldberg. English-Russian energy dictionary. 2006] Topics in the energy sector in general EN warranty assurance ...
lifetime costs (of equipment)- - [A.S. Goldberg. English-Russian energy dictionary. 2006] Topics: energy in general EN life cycle cost ... Technical Translator's Guide
technical service life of equipment- - [A.S. Goldberg. English-Russian energy dictionary. 2006] Topics of energy in general EN technological lifespan ... Technical Translator's Guide
economically viable equipment service life- - [A.S. Goldberg. English-Russian energy dictionary. 2006] Topics: energy in general EN economic life time ... Technical Translator's Guide
Service life of the thermal insulation structure- Service life of a thermal insulation structure: the calendar duration of operation of the structure from the start of operation until its transition to the limit state in accordance with GOST 27.002... Source: STANDARDS FOR DESIGNING THERMAL INSULATION... ... Official terminology
life time- 06.01.100 service life [projected life]: The period of serviceability of a radio frequency tag, expressed by the number of read and/or write cycles, and in the case of active radio frequency tags by the number of years, estimated based on the expected resource of the source... ...
service life of the thermal insulation structure- 3.32 service life of a thermal insulation structure: Calendar duration of operation of the structure from the start of operation until its transition to the limit state in accordance with GOST 27.002. Maintenance program for insulated... ... Dictionary-reference book of terms of normative and technical documentation
designated service life- 3.8 designated service life: Calendar duration of operation, upon reaching which the operation of the tub must be stopped, regardless of its technical condition.
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