Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network

Cloud-enabled prognosis for manufacturing

Failure and reliability prediction by support vector machines regression of time series data

In condition-based maintenance (CBM), effective diagnostic and prognostic tools are essential for maintenance engineers to identify imminent fault and predict the remaining useful life before the components finally fail. This enables remedial actions to be taken in advance and reschedule of production if necessary. All machine components are subjected to degradation processes in real environments and they have certain failure characteristics which can be related to the operating conditions. This paper describes a technique for accurate assessment of the remnant life of bearings based on health state probability estimation and historical knowledge embedded in the closed loop diagnostics and prognostics system. The technique uses the Support Vector Machine (SVM) classifier as a tool for estimating health state probability of machine degradation process to provide long term prediction. To validate the feasibility of the proposed model, real life fault historical data from bearings of High Pressure-Liquefied Natural Gas (HP-LNG) pumps were analysed and used to obtain the optimal prediction of remaining useful life (RUL). The results obtained were very encouraging and showed that the proposed prognosis system based on health state probability estimation has the potential to be used as an estimation tool for remnant life prediction in industrial machinery.

Bearing fault prognosis based on health state probability estimation

Dynamic selective maintenance optimization for multi-state systems over a finite horizon: A deep reinforcement learning approach

A system (machine) is observed to operate in 1 of 2 modes. The most common mode is loaded (or regular) operation. Occasionally the system is placed in an unloaded state, wherein while the system is mechanically still operating, it is assumed that the failure intensity is lower due to this reduction in operating intensity. A proportional hazards framework is used to capture this potential reduction in failure intensity due to switching of operating modes. In either operating condition, analyzed maintenance-records indicate that the system was occasionally shut down, and either a minor or a major repair was undertaken. Furthermore, despite such repairs, it is observed that both modes of operation (loaded or unloaded) resulted in random failures. On failure, 1 of 3 actions are taken: (1) failures were minimally repaired, (2) given a minor repair, or (3) given a major repair. Both minor and major repairs are assumed to impact the intensity following a virtual age process of the general form proposed by Kijima. This research develops a statistical model of such an operating/maintenance environment. Its purpose is to quantify the impacts of performing these repair actions on the failure intensities. Field data from an industrial-setting demonstrate that appropriate parameter estimates for such multiple phenomena can be obtained. Providing a richer, more detailed, modeling of the failure intensity of a system incorporating both operating conditions and repair effects has important ramifications for maintenance planning. This paper refers to related research, in which optimal timing of maintenance repairs depends fundamentally on the failure rate of the system.

A general repair, proportional-hazards, framework to model complex repairable systems

Estimation of reliability and maintainability parameters is essential to modeling repairable systems and determining maintenance policies. However, this estimation becomes more difficult when system failure times are neither identically nor independently distributed. This is due to the aging of repairable systems under imperfect maintenance. In this paper, reliability and maintainability RAM parameters are estimated in the maximum likelihood sense based on historical RAM data and using the virtual age model of Kijima Type I and Type II. A Weibull distribution for the first system failure is assumed. Kijima Type I and II imperfect corrective and preventive maintenance are also considered. Using the maximum-likelihood approach, four parameters of this repairable system are estimated. The proposed method is illustrated with simulated data.

Estimation of the parameters for a complex repairable system with preventive and corrective maintenance

In various critical applications of many systems, economic use has been an essential architectural attribute for achieving high performance. A k-out-of-n system, which consists of n components and fails if at least 
$$n-k+1$$

 of the n components fail, is one of the most important systems in practice. Several researchers have shown that, there is an optimal design of such a system, so that the expected total cost of the system is minimized. The present paper aims to find the best compromise between age replacement and system configuration to design a k-out-of-n system when replacement first and last are applied i.e. the system undergoes preventive maintenances before failure at a planned time T or at a random working cycle Y whichever occurs first or last. Expected cost rates for the first and last replacement are formulated. Case studies are provided to illustrate the applications of the theoretical results developed and a numerical algorithm is made to obtain optimal k-out-of-n system configuration.

Optimal design of k-out-of-n system under first and last replacement in reliability theory

Proposed by the Swedish engineer and mathematician Ernst Hjalmar Waloddi Weibull (1887-1979), the Weibull distribution is a probability distribution that is widely used to model lifetime data. Because of its flexibility, some modifications of the Weibull distribution have been made from several researches in order to best adjust the non-monotonic shapes. This paper gives a study on the performance of two specific modifications of the Weibull distribution which are the exponentiated Weibull distribution and the additive Weibull distribution.

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Soufiane Gasmi

Papers

A general repair, proportional-hazards, framework to model complex repairable systems

Estimation of the parameters for a complex repairable system with preventive and corrective maintenance

Optimal design of k-out-of-n system under first and last replacement in reliability theory

Parameter Estimations for Some Modifications of the Weibull Distribution

Parameter estimation in an alternating repair model