Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

Choi

2021

Sensors

Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.

“…Bond graph [112] Interaction between components Dependency on operating mode Gearbox Marshall-Olkin bivariate exponential distribution [113] Interaction between failure mode…”

Section: Hydraulic Hybrid Systemmentioning

confidence: 99%

Challenges and Opportunities of System-Level Prognostics

Choi

2021

Sensors

Proceedings of the Institution of Mechanical Engineers, Part O:

“…Ibe and Wein 22 conducted reliability modeling for the system with partial observable failure and complete observable failure, and obtained the system availability using partial observable Markov process in the case of misdiagnosis failure mode. Li et al 23 proposed a Bayesian fault detection scheme for gear system with degradation failure and sudden failure, where a three state continuous time hidden Markov model was used to describe the degradation process of the gear system, and a maintenance scheme to maximize the average availability of the system was obtained. Wang et al 24 investigated the joint optimization of condition-based and age-based replacement policy and inventory policy for a two-unit series system.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al 39 investigated reliability modeling for dependent competing failure processes of damage self-healing systems. Li et al 23 studied optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes. Zhou et al 40 proposed the copula-based method for statistical analysis of step-stress accelerated life test with dependent competing failure modes.…”

Section: Introductionmentioning

confidence: 99%

Availability analysis and maintenance optimization for multiple failure mode systems considering imperfect repair

Qiu

Liu

Lin

et al. 2021

This paper studies the availability and optimal maintenance policies for systems subject to competing failure modes under continuous and periodic inspections. The repair time distribution and maintenance cost are both dependent on the failure modes. We investigate the instantaneous availability and the steady state availability of the system maintained through several imperfect repairs before a replacement is allowed. Analytical expressions for system availability under continuous and periodic inspections are derived respectively. The availability models are then utilized to obtain the optimal inspection and imperfect maintenance policy that minimizes the average long-run cost rate. A numerical example for Remote Power Feeding System is presented to demonstrate the application of the developed approach.

“…Teng et al [18] extracted the vibration signal features of full-cycle data on a gearbox and predicted the RUL using the Hidden Markov Model. Li et al [19] considered the competing risk model within the semi-Markov decision process framework to make maintenance decisions on the early failure detection of gear systems. During the modelling process, the parameter estimation on the internal state of machines based on the monitoring value requires a consideration of the impact of noise.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Trend Index‐Driven Remaining Useful Life Prediction Method for Renewable Energy Vehicle Reducers

Chen

Weng

et al. 2021

Shock and Vibration

Renewable energy vehicle reducers are now being developed towards achieving high-speeds, high-torque, and high-integration and intelligent trends. Its performance also determines the operation state and reliability of vehicles. Therefore, it is necessary to conduct the online condition assessment and remaining useful life predictions for renewable energy vehicle reducers. In those methods, the trend index construction is one of the most crucial steps. Hence, an adaptive trend index-driven remaining useful life prediction method is proposed to conduct condition assessment and prediction of renewable energy vehicle reducers. Firstly, an adaptive trend index is constructed, where the difference of the Fourier amplitude spectrum between the initial state and the current state is calculated to present the health trend index. Secondly, the reducer’s performance degradation model is built. In order to conduct remaining useful life prediction, the particle filtering is used to update the parameters of the reducer’s performance degradation model with the constructed adaptive trend index. In order to verify the effectiveness of the proposed method, an accelerated life test is conducted on a three-motor test bed to achieve the life-cycle data of reducers. The proposed method is verified with the obtained data and compared with the commonly used ARIMA model. The test results show that the proposed method achieves better results than the traditional methods. It means that the proposed method is a potential one for the real-time monitoring of the health state of renewable energy vehicle reducers.