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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sidali Bacha ◽  
Ahmed Bellaouar ◽  
Jean-Paul Dron
Keyword(s):  
Repairable Systems ◽  
Corrective Maintenance ◽  
Content Type ◽  
The Real ◽  
Oil Pump ◽  
Wide Range ◽  
Proportional Intensity ◽  
Proportional Intensity Model ◽  
Petroleum Company ◽  
Intensity Model

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.

Robustness evaluation of a semi-parametric proportional intensity model

1994 ◽  
Vol 44 (1) ◽  
pp. 103-109 ◽  
Author(s):  
Waseem M. Qureshi ◽  
Thomas L. Landers ◽  
Edward E. Gbur
Keyword(s):  
Proportional Intensity ◽  
Proportional Intensity Model ◽  
Intensity Model

A renewal process model with failure based PM and imperfect CM for unavailability exploration

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Pavel Jahoda ◽  
Radim Bris
Keyword(s):  
Preventive Maintenance ◽  
Process Model ◽  
Aging Process ◽  
Renewal Theory ◽  
Mathematical Methods ◽  
Corrective Maintenance ◽  
Content Type ◽  
The Real ◽  
Maintenance Model ◽  
Renewal Cycle

PurposeThe paper aims to explore unavailability of dormant systems that are under both preventive and corrective maintenance. Preventive maintenance is considered as a failure based maintenance model, where full renew is realized at the occurrence of every nth failure. It proposes the imperfect corrective maintenance model, where each restoration process deteriorates the system lifetime, probability distribution of which is gradually changed via increasing failure rate.Design/methodology/approachBasic reliability mathematics necessary for unavailability quantification of a system which undergoes a real aging process with maintenance has been derived proceeding from renewal theory. New renewal cycle was defined to cover the real aging process and the expectation of its length was determined. All events resulting in the failure of studied system were explored to determine their probabilities. An integral equation where the unavailability function characterizing studied system is its solution was derived.FindingsPreventive maintenance is closely connected with the occurrence of the nth failure, which starts its renew. The number n can be considered as a parameter which significantly influences the unavailability course. The paper shows that the real aging process characterized by imperfect repairs can significantly increase the unavailability courses in contrast with theoretical aging. This is true for both monitored and dormant systems.Originality/valueAlthough mathematical methods used in this article were inspired and influenced by the work of reference (van der Weide and Pandey, 2015), derivation of final formulas for unavailability quantification considering the new renewal cycle is original. Idea of the real aging process is new as well. This paper fulfils an identified need to manage the maintenance of realistically aging systems.

Design of real-time filter for the wheel force transducer

Sensor Review ◽  
2015 ◽  
Vol 35 (2) ◽  
pp. 174-182 ◽  
Author(s):  
Dong Wang ◽  
Guoyu Lin ◽  
Weigong Zhang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Nonlinear Filtering ◽  
Unscented Kalman Filter ◽  
Operating Conditions ◽  
Test Bed ◽  
Content Type ◽  
The Real ◽  
Wide Range ◽  
Real Vehicle

Purpose – Wheel force transducers (WFTs) have performance characteristics that make them attractive for applications in endurance evaluation of road vehicles, ride and handling optimization, tire development and vehicle dynamics. As a WFT is mounted on the the driven wheel, the loads on the wheel and the outputs of WFTs are usually nonlinearly related. Thus, a real-time filter is needed to measure the true loads on the wheel. Design/methodology/approach – In this paper, a new nonlinear filtering algorithm utilizing quadrature Kalman filter (QKF) is proposed to track the actual loads in real time through establishing the specific observation equations with Singer models. Findings – The simulation results show that the accuracy and the rapidity of QKF outperforms the capability of the unscented Kalman filter (UKF). Then, the dynamic tests on the MTS testing platform give the comparisons between the real-time QKF and the wavelet transform, where the former has superior dynamic accuracy. Finally, the practical tests of shifting and braking on a real vehicle confirm the effectiveness of QKF, which further validates the proposed method fitting reality. Originality/value – In this paper, a newly improved algorithm with QKF for WFT has been proposed and tested experimentally. As the wheel loads are always time-varying and complex, introducing Gaussian noise in the outputs of the transducer, WFT-suitable Singer model and WFT measurement equation base on a QKF are established. The experiment results show that QKF has advanced performance than the traditional UKF. Also, the road wheel test bed produced by MTS has been exploited as the test platform to demonstrate the dynamic efficiency of the proposed real-time filter under various operating conditions for a wide range of loads. And, the practical tests with the real vehicle are accomplished to verify the value and effectiveness of the proposed method.

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