scholarly journals Research on Performance Degradation Assessment Method of Train Rolling Bearings under incomplete data

2016 ◽  
Author(s):  
Xuejun Zhao ◽  
Yong Qin ◽  
Dandan Wang ◽  
Zhipeng Wang ◽  
Limin Jia
Keyword(s):  
Incomplete Data ◽  
Assessment Method ◽  
Performance Degradation ◽  
Rolling Bearings

An Online Quantified Safety Assessment Method for Train Service State Based on Safety Region Estimation and Hybrid Intelligence Technologies

2015 ◽  
Vol 25 (03) ◽  
pp. 493-511
Author(s):  
Yong Qin ◽  
Shan Yu ◽  
Yuan Zhang ◽  
Limin Jia ◽  
Xiaoqing Cheng
Keyword(s):  
Safety Assessment ◽  
Safety Analysis ◽  
Assessment Method ◽  
Rolling Bearings ◽  
Safety Risk ◽  
Hybrid Intelligence ◽  
Local Mean ◽  
Interval Type ◽  
Service State

Facing the important issues of safety analysis and assessment for the train service state, an online quantified safety assessment method based on the safety region estimation and hybrid intelligence technologies was proposed in this paper. First, the previous researches on the safety analysis and assessment were briefly reviewed for the train itself and its key equipment, and the existential problems were further pointed out. Then, using the safety monitoring data and the safety region estimation theory, a new online safety assessment method with data-driven was put forward, which was followed by a detailed description of the concrete implementation steps including the EMD (Local Mean Decomposition) and EM (Energy Moment) based safety risk evaluation index selection, Interval Type 2 Fuzzy C-Means (IT2FCM) clustering based safety region boundary calculation modeling and safety risk grading. Finally, in order to verify its performance through experiments, the above method was applied in analyzing and evaluating service states of the rolling bearings, the key equipment of the train, on the basis of mass field data. The experimental results indicate that this method is valid.

A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems

2018 ◽  
Vol 233 (3) ◽  
pp. 369-378 ◽  
Author(s):  
Wenjie Dong ◽  
Sifeng Liu ◽  
Zhigeng Fang ◽  
Yingsai Cao ◽  
Ye Ding
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Network Model ◽  
State Transition ◽  
Likelihood Function ◽  
Hidden Markov ◽  
Reliability Assessment ◽  
Assessment Method ◽  
Performance Degradation ◽  
State System

The essence of multi-state system performance degradation is a process of deteriorating state transition. On the basis of hidden Markov model and graphic evaluation and review technique network, this article proposes a new reliability assessment method called hidden graphic evaluation and review technique network model for multi-state system. Specifically, nodes in graphic evaluation and review technique network represent hidden states of a system at different deteriorating times, and they can be expanded through a series of observable sequences. Baum–Welch algorithm in hidden Markov model is introduced to train parameters and when logarithmic likelihood function of the output reaches convergent, we can estimate the most probable output state and obtain the state transition probability eventually. Suppose performance degradation amount between different nodes follows gamma distribution, a method of improved maximum likelihood function is introduced to estimate parameters. According to signal flow graph theory and Mason formula, equivalent transfer function from the initial node to any other nodes can be obtained, then expectation and variance of performance degradation amount can be presented. In the real case study, we compare the reliability assessment method proposed in this article with other two traditional methods, which show the rationality of hidden graphic evaluation and review technique network model.

scholarly journals Bearing Performance Degradation Assessment Using Lifting Wavelet Packet Symbolic Entropy and SVDD

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jianmin Zhou ◽  
Huijuan Guo ◽  
Long Zhang ◽  
Qingyao Xu ◽  
Hui Li
Keyword(s):  
Wavelet Packet ◽  
Assessment Method ◽  
Performance Degradation ◽  
Data Repository ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Mode Decomposition ◽  
Lifting Wavelet ◽  
Envelope Spectrum

Bearing performance degradation assessment is of great significance for proactive maintenance and near-zero downtime. For this purpose, a novel assessment method is proposed based on lifting wavelet packet symbolic entropy (LWPSE) and support vector data description (SVDD). LWPSE is presented for feature extraction by jointing use of lifting wavelet packet transform and symbolic entropy. Firstly, the LWPSEs of bearing signals from normal bearing condition are extracted to train an SVDD model by fitting a tight hypersphere around normal samples. Then, the relative distance from the LWPSEs of testing signals to the hypersphere boundary is calculated as a quantitative index for bearing performance degradation assessment. The feasibility and efficiency of the proposed method were validated by the life-cycle data obtained from NASA’s prognostics data repository and the comparison with Hidden Markov Model (HMM). Finally, the assessment results were verified by the envelope spectrum analysis method based on empirical mode decomposition and Hilbert envelope demodulation.

An intelligent performance degradation assessment method for bearings

2016 ◽  
Vol 23 (18) ◽  
pp. 3023-3040 ◽  
Author(s):  
Huiming Jiang ◽  
Jin Chen ◽  
Guangming Dong ◽  
Ran Wang
Keyword(s):  
Evaluation System ◽  
Health Management ◽  
Feature Space ◽  
Assessment Method ◽  
Performance Degradation ◽  
Accelerated Life Test ◽  
Time Data ◽  
Accelerated Life ◽  
Rolling Element ◽  
Close Relationship

Bearings are one of the most frequently used components in the rotatory machinery, so the performance degradation assessment of bearings plays an important role in the prognostics and health management of systems. Hidden Markov model (HMM) is a widely applied data-driven model used for bearing performance degradation assessment and has many successful applications. A normal HMM needs to be trained in advance, which has close relationship with the evaluation system. However, the trained HMM is quite influenced by many issues, such as the data integrity and the feature space. In this paper, an intelligent bearing performance degradation assessment method based on HMM and nuisance attribute projection (NAP) is proposed. The proposed method can combine the information from the experimental data and the real-time data effectively and assess the performance since the beginning of the monitoring. The effectiveness of the proposed method is verified through an accelerated life test of rolling element bearings.

Reliability Assessment Method of the System with Multiple Degradation Parameters Based on SVDD and SVR

2011 ◽  
Vol 80-81 ◽  
pp. 1187-1192
Author(s):  
Hua Cong ◽  
Guang Ping Wu ◽  
Fu Zhou Feng ◽  
Guo Qiang Rao
Keyword(s):  
Diesel Engine ◽  
Distance Measure ◽  
Reliability Assessment ◽  
Engine Performance ◽  
Assessment Method ◽  
Performance Degradation ◽  
Assessment Model ◽  
Vehicle Tracking ◽  
Performance Parameters ◽  
Distribution Parameters

This paper presents a reliability assessment method for the system with multiple degradation parameters based on SVDD and SVR. The method transforms different performance parameters of the system to a distance measure by SVDD, uses SVR to analyze and predict the distribution parameters of SVDD distance, then create the overall system performance degradation assessment model. Through vehicle tracking experiment, collecting different degradation parameters of the diesel engine, a reliability curve stands for the whole system degradation is achieved. Compared to traditional reliability assessment method, the new method combines the various parameters so that more effectively describes the diesel engine performance degradation, and is more instructive for the maintenance of the vehicle.

Multi-Mode Particle Filter for Bearing Remaining Life Prediction

2018 ◽  
Author(s):  
Peng Wang ◽  
Ruqiang Yan ◽  
Robert X. Gao
Keyword(s):  
Service Life ◽  
Particle Filter ◽  
Individual Performance ◽  
Performance Degradation ◽  
Remaining Useful Life ◽  
Normal Operation ◽  
Life Stages ◽  
Rolling Bearings ◽  
Useful Life ◽  
Multi Mode

As a critical element in rotating machines, remaining useful life (RUL) prediction of rolling bearings plays an essential role in realizing predictive and preventative machine maintenance in modern manufacturing. The physics of defect (e.g. spall) initiation and propagation describes bearing’s service life as generally divided into three stages: normal operation, defect initiation, and accelerated performance degradation. The transition among the stages are embedded in the variations of monitored data, e.g., vibration. This paper presents a multi-mode particle filter (MMPF) that is aimed to: 1) automatically detect the transition among the three life stages; and 2) accurately characterize bearing performance degradation by integrating physical models with stochastic modeling method. In MMPF, a set of linear and non-linear modes (also called degradation functions) are first defined according to the physical/empirical knowledge as well as statistical analysis of the measured data (e.g. vibration). These modes are subsequently refined during the particle filtering (PF)-based bearing performance tracking process. Each mode corresponds to an individual performance scenario. A finite-state Markov chain switches among these modes, reflecting the transition between the service life stages. Case studies performed on two run-to-failure experiments indicate that the developed technique is effective in tracking the evolution of bearing performance degradation and predicting the remaining useful life of rolling bearings.

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