Reliability analysis of hydraulic systems of LHD machines using the power law process model

Earlier, a preliminary study of the reliability characteristics of a fleet of load-haul-dump (LHD) machines deployed at Kiruna mine showed that the engine and the hydraulics are the two most critical subsystems. Hydraulic systems are selected for further study because such systems are still under a development phase. Maintenance data for two years for these machines are analyzed. The tests for trends and serial correlation showed that times between successive failures for the hydraulic systems are in most cases not independent and identically distributed. Goodness-of-fit tests showed that the power law process model provides a good fit to the failure data of the hydraulic systems. Methods for parameter estimation in the power law process model and estimation of optimal maintenance intervals for such systems are presented. Emphasis is on the use of graphical methods for data analysis.     

Failure event prediction using the Cox proportional hazard model driven by frequent failure signatures

The analysis of event sequence data that contains system failures is becoming increasingly important in the design of service and maintenance policies. This paper presents a systematic methodology to construct a statistical prediction model for failure event based on event sequence data. First, frequent failure signatures, defined as a group of events/errors that repeatedly occur together, are identified automatically from the event sequence by use of an efficient algorithm. Then, the Cox proportional hazard model, that is extensively used in biomedical survival analysis, is used to provide a statistically rigorous prediction of system failures based on the time-to-failure data extracted from the event sequences. The identified failure signatures are used to select significant covariates for the Cox model, i.e., only the events and/or event combinations in the signatures are treated as explanatory variables in the Cox model fitting. By combining the failure signature and Cox model approaches the proposed method can effectively handle the situation of a long event sequence and a large number of event types in the sequence. Its effectiveness is illustrated by a numerical study and analysis of real-world data. The proposed method can help proactively diagnose machine faults with a sufficient lead time before actual system failures to allow preventive maintenance to be scheduled thereby reducing the downtime costs.     

Reliability Assessment of Cogeneration Power Plant in Textile Mill Using Fault Tree Analysis

The Combined Heat and Power (CHP) Systems are systems that simultaneously generate both electricity and useful heat. It is important to analyze the reliability of these systems to ensure the lowest level of life cycle cost. A CHP system installed in a textile mill is considered as a case study to assess the reliability through fault tree analysis (FTA). The common cause failures (CCFs) are evaluated using the β-factor model with the available data on the failure of the plant. On a detailed analysis, it is found that the unavailability of the plant is 8.50E−03, which is predominantly caused by the problems related to mechanical system, subsystems of boiler, and turbine. The repair and the restoration times for these components used in the fault tree analysis (FTA) are 48 and 8 h, respectively. Hence, faster restoration of these components affected by shutdown/failure and implementation of reliability-centered maintenance (RCM) features will significantly improve the reliability of the system, thereby reducing the time with respect to return on the investment.     

Transparent reliability model for fault-tolerant safety systems

A reliability model is presented which may serve as a tool for identification of cost-effective configurations and operating philosophies of computer-based process safety systems. The main merit of the model is the explicit relationship in the mathematical formulas between failure cause and the means used to improve system reliability such as self-test, redundancy, preventive maintenance and corrective maintenance. A component failure taxonomy has been developed which allows the analyst to treat hardware failures, human failures, and software failures of automatic systems in an integrated manner. Furthermore, the taxonomy distinguishes between failures due to excessive environmental stresses and failures initiated by humans during engineering and operation. Attention has been given to develop a transparent model which provides predictions which are in good agreement with observed system performance, and which is applicable for non-experts in the field of reliability.     

Utility based maintenance analysis using a Random Sign censoring model

Industrial systems subject to failures are usually inspected when there are evident signs of an imminent failure. Maintenance is therefore performed at a random time, somehow dependent on the failure mechanism. A competing risk model, namely a Random Sign model, is considered to relate failure and maintenance times. We propose a novel Bayesian analysis of the model and apply it to actual data from a water pump in an oil refinery. The design of an optimal maintenance policy is then discussed under a formal decision theoretic approach, analyzing the goodness of the current maintenance policy and making decisions about the optimal maintenance time.     

Optimal maintenance scheduling of power producers considering unexpected unit failure

A novel unit maintenance scheduling (UMS) problem formulation for a generation producer is presented, to maximise its benefit while thoroughly considering the risk associated with unexpected unit failures. First, the unit failure is characterised by a more practical bathtub-shaped failure behaviour from the modified superposed power law process. Its parameters are estimated from the historical data by solving a nonlinear least-squares-fitting problem via Gauss-Newton iteration method. On the basis of the unit failure analysis, the new UMS formulation is solved by a combination of linear programming and genetic algorithms (GAs), and its impact on the producer-s benefit is analysed in detail, including expected profit of selling energy, expected renewal cost of damaged components and maintenance cost. Compared with the current models, the proposed UMS model takes into consideration the influences of market factors as well as unexpected unit failures to strike the right balance between profits and costs related with potential unit failures. Numerical examples on a four-unit producer are utilised to demonstrate the effectiveness of the proposed scheme.     

Uptime of Systems Subject to Repairable and Nonrepayable Failures

Expressions are derived for the distribution and expected value of uptime for systems subject to both repairable and nonrepairable failures. The results are applicable to a wide range of situations, including the analysis of systems subject to major structural failure, product or process obsolescence, or failure of critical nonrepairable subsystems. Several examples are investigated, including multiple component series systems, systems containing standby redundant nonrepairable subsystems and systems containing standby redundant repairable subsystems.     

A Dynamic Risk Assessment Methodology for Maintenance Decision Support

The failure mode and effect analysis (FMEA) is a widely applied technique for prioritizing equipment failures in the maintenance decision‐making domain. Recent improvements on the FMEA have largely focussed on addressing the shortcomings of the conventional FMEA of which the risk priority number is incorporated as a measure for prioritizing failure modes. In this regard, considerable research effort has been directed towards addressing uncertainties associated with the risk priority number metrics, that is occurrence, severity and detection. Despite these improvements, assigning these metrics remains largely subjective and mostly relies on expert elicitations, more so in instances where empirical data are sparse. Moreover, the FMEA results remain static and are seldom updated with the availability of new failure information. In this paper, a dynamic risk assessment methodology is proposed and based on the hierarchical Bayes theory. In the methodology, posterior distribution functions are derived for risk metrics associated with equipment failure of which the posterior function combines both prior functions elicited from experts and observed evidences based on empirical data. Thereafter, the posterior functions are incorporated as input to a Monte Carlo simulation model from which the expected cost of failure is generated and failure modes prioritized on this basis. A decision scheme for selecting appropriate maintenance strategy is proposed, and its applicability is demonstrated in the case study of thermal power plant equipment failures. Copyright © 2016 John Wiley & Sons, Ltd.     

Maintenance Strategies and Failure‐Cost Model for Medical Equipment

This paper proposes a mathematical maintenance model that analyses the effect of maintenance on the survival probability of medical equipment based on maintenance history and age of the equipment. The proposed model is simulated in Scilab using real data extracted from maintenance history of anaesthesia machine from Draeger. The analysis using survival approach reveals that conducting preventive maintenance on the selected medical equipment had a positive impact on survival of equipment. The model is then used to analyse the cost of maintenance scenarios, and an appropriate scenario is proposed for anaesthesia machine. A new failure‐cost model is developed, which may be used to calculate the number of failures of equipment and the annual maintenance cost. The proposed models may be used as a planning tool for selecting maintenance strategies for various medical equipments. Copyright © 2014 John Wiley & Sons, Ltd.     

Reliability modeling of multi‐state degraded repairable systems and its applications to automotive systems

In this paper, we presented a continuous‐time Markov process‐based model for evaluating time‐dependent reliability indices of multi‐state degraded systems, particularly for some automotive subsystems and components subject to minimal repairs and negative repair effects. The minimal repair policy, which restores the system back to an “as bad as old” functioning state just before failure, is widely used for automotive systems repair because of its low cost of maintenance. The current study distinguishes with others that the negative repair effects, such as unpredictable human error during repair work and negative effects caused by propagated failures, are considered in the model. The negative repair effects may transfer the system to a degraded operational state that is worse than before due to an imperfect repair. Additionally, a special condition that a system under repair may be directly transferred to a complete failure state is also considered. Using the continuous‐time Markov process approach, we obtained the general solutions to the time‐dependent probabilities of each system state. Moreover, we also provided the expressions for several reliability measures include availability, unavailability, reliability, mean life time, and mean time to first failure. An illustrative numerical example of reliability assessment of an electric car battery system is provided. Finally, we use the proposed multi‐state system model to model a vehicle sub‐frame fatigue degradation process. The proposed model can be applied for many practical systems, especially for the systems that are designed with finite service life.     

A corrective maintenance scheme for engineering equipment

Corrective maintenance is a maintenance task performed to identify and rectify the cause failures for a failed system. The engineering equipment gets many components and failure modes, and its failure mechanism is very complicated. Failure of system-level might occur due to failure(s) of any subsystem/component. Thus, the symptom failure of equipment may be caused by multilevel causality of latent failures.This paper proposes a complete corrective maintenance scheme for engineering equipment. Firstly, the FMECA is extended to organize the numerous failure modes. Secondly, the failure propagation model (FPM) is presented to depict the cause-effect relationship between failures. Multiple FPMs will make up the failure propagation graph (FPG). For a specific symptom failure, the FPG is built by iteratively searching the cause failures with FPM. Moreover, when some failure in the FPG is newly ascertained to occur (or not), the FPG needs to be adjusted. The FPG updating process is proposed to accomplish the adjustment of FPG under newly ascertained failure. Then, the probability of the cause failures is calculated by the fault diagnosis process. Thirdly, the conventional corrective maintenance recommends that the failure with the largest probability should be ascertained firstly. However, the proposed approach considers not only the probability but also the failure detectability and severity. The term REN is introduced to measure the risk of the failure. Then, a binary decision tree is trained based on REN reduction to determine the failure ascertainment order. Finally, a case is presented to implement the proposed approach on the ram feed subsystem of a boring machine tool. The result proves the validity and practicability of the proposed method for corrective maintenance of engineering equipment.     

Modeling the bathtub shape hazard rate function in terms of reliability

In this paper, a general form of bathtub shape hazard rate function is proposed in terms of reliability. The degradation of system reliability comes from different failure mechanisms, in particular those related to (1) random failures, (2) cumulative damage, (3) man–machine interference, and (4) adaptation. The first item is referred to the modeling of unpredictable failures in a Poisson process, i.e. it is shown by a constant. Cumulative damage emphasizes the failures owing to strength deterioration and therefore the possibility of system sustaining the normal operation load decreases with time. It depends on the failure probability, 1−R. This representation denotes the memory characteristics of the second failure cause. Man–machine interference may lead to a positive effect in the failure rate due to learning and correction, or negative from the consequence of human inappropriate habit in system operations, etc. It is suggested that this item is correlated to the reliability, R, as well as the failure probability. Adaptation concerns with continuous adjusting between the mating subsystems. When a new system is set on duty, some hidden defects are explored and disappeared eventually. Therefore, the reliability decays combined with decreasing failure rate, which is expressed as a power of reliability. Each of these phenomena brings about the failures independently and is described by an additive term in the hazard rate function h(R), thus the overall failure behavior governed by a number of parameters is found by fitting the evidence data. The proposed model is meaningful in capturing the physical phenomena occurring during the system lifetime and provides for simpler and more effective parameter fitting than the usually adopted ‘bathtub’ procedures. Five examples of different type of failure mechanisms are taken in the validation of the proposed model. Satisfactory results are found from the comparisons.     

Reliability improvement through alternative designs—A case study

In today's competitive world, reliability of equipment is extremely important to maintain quality and delivery deadlines. This is achieved by using proper maintenance and design changes for unreliable subsystems and components of a complex system. It is significant to develop a strategy for maintenance, replacement and design changes related to those subsystems and components. An analysis of down time along with causes is essential to identify the unreliable components and subsystems.This paper presents an analysis of failure data of solenoid coils of automatic internal grinding machine used in a bearing manufacturing plant. It analyses various replacement and change of design options such as introduction of pneumatic system in place of electromagnetic solenoids for improvement of reliability of the plunger movement mechanism.     

Optimal maintenance policies in incomplete repair models

We consider an incomplete repair model, that is, the impact of repair is not minimal as in the homogeneous Poisson process and not “as good as new” as in renewal processes but lies between these boundary cases. The repairs are assumed to impact the failure intensity following a virtual age process of the general form proposed by Kijima. In previous works field data from an industrial setting were used to fit several models. In most cases the estimated rate of occurrence of failures was that of an underlying exponential distribution of the time between failures. In this paper, it is shown that there exist maintenance schedules under which the failure behavior of the failure-repair process becomes a homogeneous Poisson process.     

Forecasting reliability growth

This paper describes a method for estimating and forecasting reliability from attribute data, using the binomial model, when reliability requirements are very high and test data are limited. Integer data—specifically, numbers of failures — are converted into non-integer data. The rationale is that when engineering corrective action for a failure is implemented, the probability of recurrence of that failure is reduced; therefore, such failures should not be carried as full failures in subsequent reliability estimates. The reduced failure value for each failure mode is the upper limit on the probability of failure based on the number of successes after engineering corrective action has been implemented. Each failure value is less than one and diminishes as test programme successes continue. These numbers replace the integral numbers (of failures) in the binomial estimate. This method of reliability estimation was applied to attribute data from the life history of a previously tested system, and a reliability growth equation was fitted. It was then ‘calibrated’ for a current similar system's ultimate reliability requirements to provide a model for reliability growth over its entire life-cycle. By comparing current estimates of reliability with the expected value computed from the model, the forecast was obtained by extrapolation.     

The modelling of degraded and critical failures for components with dormant failures

The failures reported in reliability data bases are often classified into sseverity classes, e.g., as critical or degraded failures. This paper presents models for the failure mechanism causing the degraded and critical failures, and estimators for the failure intensities of the models are provided. The discussions mainly focus on dormant (hidden) failures of a standby component. The suggested models are based on exponentially distributed random variables, but they give non-exponential (phase-type) distributions for the time to failure, and thus provide alternatives to the more common Weibull model. The main model is adapted to the information available in modern reliability data bases. Using this model it is also possible to quantify the reduction in the rate of critical failures, achieved by repairing degraded failures. In particular the so-called ‘naked failure rate’ (defined as the rate of critical failures that would be observed if no repair of degraded failures was carried out) is derived. Further, the safety unavailability (Mean Fractional Deadtime) of a dormant system is obtained for the new model.     

A graphical approach for confidence limits of optimal preventive maintenance cycles

Facilities management (FM) is the management of infrastructure resources and services to support and sustain the operational strategy of an organization over time. Maintenance is often the business process that has not been optimized and is considered as a liability of business operations. Therefore, extensive studies have been done to determine the optimal replacement interval for irreparable parts of repairable systems where typically the time between failures is characterized by lifetime distribution in which the parameters are estimated from failure data. As a result, the optimal preventive maintenance (PM) interval computed is exposed to sampling risk as the repair cost and failure data used for estimation are typically highly censored due to issues related to data collection and unobserved failures. In this paper, we present a graphical approach to obtain the confidence interval for the optimal PM interval that resulted from sampling variations parameter estimates. The proposed methodology is applied in the context of FM as a strategy for opportunistic replacement and for the purpose of validating the cost components in maintenance. Copyright © 2008 John Wiley & Sons, Ltd.     

The analysis of failure data in the presence of critical and degraded failures

Reported failures are often classified into severityclasses, e.g., as critical or degraded. The critical failures correspond to loss of function(s) and are those of main concern. The rate of critical failures is usually estimated by the number of observed critical failures divided by the exposure time, thus ignoring the observed degraded failures. In the present paper failure data are analyzed, applying an alternative estimate for the critical failure rate, also taking the number of observed degraded failures into account. The model includes two alternative failure mechanisms, one being of the shock type, immediately leading to a critical failure, another resulting in a gradual deterioration, leading to a degraded failure before the critical failure occurs. Failure data on safety valves from the OREDA (Offshore REliability DAta) data base are analyzed using this model. The estimate for the critical failure rate is obtained and compared with the standard estimate.     

Performance modeling in critical engineering systems using RAM analysis

Reliability, availability and maintainability (RAM) analysis of system is helpful in carrying out design modifications, if any, required to achieve minimum failures or to increase mean time between failures (MTBF) and thus to plan maintainability requirements, optimize reliability and maximize equipment availability. To this effect, the paper presents the application of RAM analysis in a process industry. Markovian approach is used to model the system behavior. For carrying out analysis, transition diagrams for various subsystems are drawn and differential equations associated with them are formulated. After obtaining the steady state solution the corresponding values of reliability and maintainability are estimated at different mission times. The computed results are presented to plant personnel for their active consideration. The results proved helpful to them for analyzing the system behavior and thereby to improve the system performance considerably by adopting and practicing suitable maintenance policies/strategies.