Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

Li, Xiaochuan; Elasha, Faris; Shanbr, Suliman; David, Éric

doi:10.3390/en12142705

Cited by 28 publications

(15 citation statements)

References 21 publications

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“…Adequate knowledge about the modal properties of a structure immensely paves way for design upgrades, faults diagnosis and remaining useful life enhancement [45,46]. Based on this premise, the impact-response method of experimental modal analysis was also used here to establish an understanding of the dynamic behaviours of the rig as well as validate the origins of subsequent faults diagnostic [47][48][49][50] features (especially spectral peaks). During the experimental modal testing, the complete rig assembly was excited by an ICP-PCB type instrumented hammer and the corresponding vibration responses were measured using the accelerometers.…”

Section: Dynamic Characteristicsmentioning

confidence: 99%

Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults

Cao

Yunusa‐Kaltungo

2020

Energies

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Rotating machines are pivotal to the achievement of core operational objectives within various industries. Recent drives for developing smart systems coupled with the significant advancements in computational technologies have immensely increased the complexity of this group of critical physical industrial assets (PIAs). Vibration-based techniques have contributed significantly towards understanding the failure modes of rotating machines and their associated components. However, the very large data requirements attributable to routine vibration-based fault diagnosis at multiple measurement locations has led to the quest for alternative approaches that possess the capability to reduce faults diagnosis downtime. Initiatives aimed at rationalising vibration-based condition monitoring data in order to just retain information that offer maximum variability includes the combination of coherent composite spectrum (CCS) and principal components analysis (PCA) for rotor-related faults diagnosis. While there is no doubt about the potentials of this approach, especially that it is independent of the number of measurement locations and foundation types, its over-reliance on manual classification made it prone to human subjectivity and lack of repeatability. The current study therefore aims to further enhance existing CCS capability in two facets—(1) exploration of the possibility of automating the process by testing its compatibility with various machine learning techniques (2) incorporating spectrum energy as a novel feature. It was observed that artificial neural networks (ANN) offered the most accurate and consistent classification outcomes under all considered scenarios, which demonstrates immense opportunity for automating the process. The paper describes computational approaches, signal processing parameters and experiments used for generating the analysed vibration data.

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Section: Dynamic Characteristicsmentioning

confidence: 99%

Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults

Cao

Yunusa‐Kaltungo

2020

Energies

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“…A framework for estimating the RUL of mechanical systems is proposed, which is composed of the multi-layer perceptron and multilayer perceptron and evolutionary algorithm for optimizing parameters [27]. Besides, there are many other machine learning algorithms, such as neural networks [28]- [30], capsule neural networks [31], dynamic Bayesian networks [32] and so on.…”

Section: New Faultmentioning

confidence: 99%

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

Yang

Sun

2019

IEEE Access

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Remaining useful life (RUL) is the premise and basis of the equipment health management plan. As accurate as possible life prediction is of great significance to reliability and economy of equipment maintenance. In this paper, a data-driven improved particle filter (PF) RUL prediction method is proposed. A health indicator extraction method based on multi-feature fusion is introduced for the RUL prediction, which can visually show the degradation trend of the healthy state of the equipment. The degradation model and observation model of equipment health indicators are established, and the PF algorithm is used to track parameters of the model. A quantum genetic algorithm is employed to improve the problem of particle degradation in PF. On the basis of filter tracking, long short term memory (LSTM) network is used to predict the trend of model coefficients, which further improves the accuracy of RUL prediction. The experiment using the C-MAPSS data set shows the proposed method has a better prediction accuracy than other methods. INDEX TERMS Remaining useful life, particle filter, quantum genetic algorithm, long short term memory.

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“…This is perhaps the reason for the surge in the popularity of predictive and condition-based maintenance (CBM) strategies [ 10 , 11 , 12 ], whereby industrial assets dictate the frequency of maintenance interventions. Just as gearboxes have earned themselves the status of inevitability within most industrial operations, vibration monitoring (VM) [ 13 , 14 , 15 ] is arguably one of the most widely applied CBM techniques owing to the established fact that all structures (static or rotating) exhibit their own peculiar individual dynamic characteristics. The fundamental premise of VM is to adequately understand, track, and determine the trend of these characteristics for individual critical assets, so as to determine deviations at incipient stages before the occurrence of catastrophic failures.…”

Section: Introductionmentioning

confidence: 99%

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

Cao

Yunusa‐Kaltungo

2021

Sensors

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The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.

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Remaining Useful Life Prediction of Rolling Element Bearings Using Supervised Machine Learning

Cited by 28 publications

References 21 publications

Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults

Towards Developing an Automated Faults Characterisation Framework for Rotating Machines. Part 1: Rotor-Related Faults

An Improved PF Remaining Useful Life Prediction Method Based on Quantum Genetics and LSTM

An Automated Data Fusion-Based Gear Faults Classification Framework in Rotating Machines

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