Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression

Rai, Akhand

doi:10.1177/0954406217700180

Cited by 62 publications

(40 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition to the choice of the specific degradation process models, there are other key aspects in RUL prediction. The first regards the definition of a proper HI that can be used to track the fault progression [30]. This can be one of the extracted features, a combination of the extracted features, or a correlation coefficient between vibration signals that corresponds to different component conditions [31].…”

Section: Degradation Process Modelling and Rul Predictionmentioning

confidence: 99%

Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems

et al. 2020

View full text Add to dashboard Cite

Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied “from scratch”; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising.

show abstract

Section: Degradation Process Modelling and Rul Predictionmentioning

confidence: 99%

Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In fact, equipment fault is not only a state, but also a developing process from normal stage to fault initiation, then to fault deterioration and then to final failure. Performance degradation assessment (PDA) pays much attention to analyzing the current state and the trend of the whole life process [2], [3]. While realizing fault diagnosis, it is more conducive to the later life prediction.…”

Section: Introductionmentioning

confidence: 99%

A Robust Performance Degradation Modeling Approach Based on Student’s t-HMM and Nuisance Attribute Projection

et al. 2020

View full text Add to dashboard Cite

Performance degradation assessment (PDA) is of great significance to ensure safety and availability of mechanical equipment. As an important issue of PDA, the robustness of the trained model directly affects the assessment efficiency and restricts its application in practice. This paper proposes a robust modeling approach based on Student's t-hidden Markov model (Student's t-HMM) and nuisance attribute projection (NAP). NAP can remove nuisance attributes caused by individual differences from the feature space. Student's t-HMM utilizes the finite Student's t-mixture models (SMMs) to describe the observation emission densities associated with each hidden state, which can be more tolerant towards outliers than conventional HMMs. Based on these two techniques, the proposed method is supposed to be more robust and can assess the performance degradation process of new objects based on data of tested objects. The superiority of the proposed approach is evaluated using the vibration data from the accelerated life tests of bearings and the public XJTU-SY Bearing Datasets. The results demonstrate the robustness and effectiveness of the proposed approach for PDA modeling of rolling element bearings.

show abstract

“…Wang and Tsui [16] established a generalized dimensionless health indicator to evaluate bearing degradation. Rai and Upadhyay [17] pro-posed a performance degradation index based on self-organizing mapping to estimate the remaining service life.…”

Section: Introductionmentioning

confidence: 99%

“… (17),. the distances from the worn state to four ideal states D + m1 , D + m2 , D + m3 , D + m4 and the closeness can be obtained as shown in…”

mentioning

confidence: 99%

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

Huang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In order to track and grasp the operation situations of the gearboxes, the vertical vibration signals of three different gear fault states, normal, worn and broken teeth, are collected via a gearbox vibration experiment. An online diagnosis and performance evaluation model with hidden Markov model (HMM) and fuzzy comprehensive evaluation is proposed. To address the limitation of maximum membership principle in the case of equal membership or the membership is very close to each other, a closeness evaluation strategy is proposed by defining the likelihood ratio of HMM as a similarity and selecting an combined membership function of the semi-trapezoidal and intermediate-ridge distribution. Results show that the online diagnosis has achieved a good performance with the similarity strategy. Compared with the evaluation strategy of the maximum membership principle, the proposed gearbox performance model with the closeness evaluation strategy is more accurately distinguished from the evaluation results of the broken teeth state and the worn state, especially for the case of the equal membership.

show abstract

Intelligent bearing performance degradation assessment and remaining useful life prediction based on self-organising map and support vector regression

Cited by 62 publications

References 35 publications

Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems

Unsupervised Fault Detection and Prediction of Remaining Useful Life for Online Prognostic Health Management of Mechanical Systems

A Robust Performance Degradation Modeling Approach Based on Student’s t-HMM and Nuisance Attribute Projection

A Fuzzy Performance Evaluation Model for a Gearbox System Using Hidden Markov Model

Contact Info

Product

Resources

About