Assessment of the effectiveness of corrective maintenance of an oil pump using the proportional intensity model (PIM)

PurposeComplex repairable systems (CRSs) are generally modeled by stochastic processes called “point processes.” These are generally summed up in the nonhomogeneous Poisson process (NHPP) and the renewal process (RP), which represent the minimum and maximum repair, respectively. However, the industrial environment affects systems in some way. This is why the main objective of this work is to model the CRS with a concept reflecting the real state of the system by incorporating an indicator in the form of covariate. This type of model, known as the proportional intensity model (PIM), will be analyzed with simulated failure data to understand the behavior of the failure process, and then it will be tested for real data from a petroleum company to evaluate the effectiveness of corrective actions carried out.Design/methodology/approachTo solve the partial repair modeling problem, the PIM was used by introducing, on the basis of the NHPP model, a multiplicative scaling factor, which reflects the degree of efficiency after each maintenance action. Several values of this multiplicative factor will be considered to generate data. Then, based on the reliability and maintenance history of 12-year pump's operation obtained from the SONATRACH Company (south industrial center (CIS), Hassi Messaoud, Algeria), the performance of the PIM will be judged and compared with the model of NHPP and RP in order to demonstrate its flexibility in modeling CRS. Using the maximum likelihood approach and relying on the Matlab software, the best fitting model should have the largest likelihood value.FindingsThe use of the PIM allows a better understanding of the physical situation of the system by allowing easy modeling to apply in practice. This is expressed by the value which, in this case, represents an improvement in the behavior of the system provided by a good quality of the corrective maintenance performed. This result is based on the hypothesis that modeling with the PIM can provide more clarification on the behavior of the system. It can indicate the effectiveness of the maintenance crew and guide managers to confirm or revise their maintenance policy.Originality/valueThe work intends to reflect the real situation in which the system operates. The originality of the work is to allow the consideration of covariates influencing the behavior of the system during its lifetime. The authors focused on modeling the degree of repair after each corrective maintenance performed on an oil pump. Since PIM does not require a specific reliability distribution to apply it, it allows a wide range of applications in the various industrial environments. Given the importance of this study, the PIM can be generalized for more covariates and working conditions.

A NEW APPROACH TO MODELING CONDITION-BASED MAINTENANCE FOR STOCHASTICALLY DETERIORATING SYSTEMS

This paper develops the degradation and maintenance modeling technique for repairable systems subject to deterioration due to aging and damage caused by operating environment factors. This provides a relaxed and generalized approach to modeling condition-based maintenance. The approach can deal with both the maintenance scheduling problem and failures preventing the system functioning further. Controlled by a virtual age process and a general class of process called Piecewise-Stochastic (Damage) process (PSP), the proportional intensity model (PIM) is used to describe the system state. The state of the system is monitored at periodic times and maintenance actions are carried out in response to the system state revealed at inspection times. Assuming a threshold-type policy, the approach aims at minimizing the long-run average cost per unit time subject to maintenance parameters; the inspection interval and preventive replacement threshold. To this end, given some assumptions, expressions for the expected cycle length and expected cost per cycle emerging as solutions of the Fredholm integral equations are obtained. The solution technique has been presented for the case when the effect of operating environment is modeled as a Gamma process.

Study on Modelling Random Deterioration Process for Complex Repairable System

Traditional probability statistics theory is impossible to obtain failure lifetime data by accelerated test for expensive and complex systems or equipments for real-time work. Due to the variety of system failure modes and the randomness of system deterioration process and the fuzziness of system maintenance threshold, it is difficult to estimate the random deterioration process of a complex repairable system by single parameter. In order to describe system performance deterioration more subjectly, it proposes generalized proportional intensity model(GPIM). considering the effects of various covariates such as performance parameters, environment stress, failure types and maintenance history simultaneously. This method provides a new method to solve the maintenance decision-making problem of complex repairable system. CF6-80C2A5 aero-engine is illustrated as an example for case study to indicate the obvious practical value by the method proposed herein.