Remaining Useful Life Estimation of Rotating Machines through Supervised Learning with Non-Linear Approaches

Chelmiah, Eoghan T.; McLoone, Violeta I.; Kavanagh, Darren F.

doi:10.3390/app12094136

Cited by 6 publications

(5 citation statements)

References 76 publications

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“…These algorithms vary in complexity and efficiency and often incorporate supervised ML approaches such as k-Nearest Neighbour (k-NN) and Support Vector Machines (SVM) [3,15,16], regression model approaches [14,39], deep learning models including convolutional neural networks (CNN) and deep belief networks (DBN) [41,42], blind deconvolution methods [43,44], and Bayesian probabilistic prediction models such as Kalman and particle filtering [45,46]. This paper introduces a low complexity prognostic monitoring approach for RUL estimation of mechanical rolling element bearings that builds further on previous work by the authors, which achieved RUL classification accuracy percentages of 74.3% using signal EA combined with novel feature engineering techniques [1]. This paper's proposed methods incorporate novel feature engineering techniques comprised of non-linear timefrequency signal processing methods, to effectively reduce the computational complexity by lowering the dimensionality of the bearing vibration signals.…”

Section: Introductionmentioning

confidence: 86%

“…Also, preventing abrupt failure modes from occurring significantly reduces health and safety concerns associated with electric and rotating machines. Catastrophic equipment failure for various mission-critical systems, such as aircraft and electrified vehicles can have serious health and safety consequences [1][2][3]. Accurate prediction of the remaining useful life (RUL) of components significantly reduces the potential for sudden failure modes to occur, allowing for timely repairs.…”

Section: Introductionmentioning

confidence: 99%

“…Data-driven bearing prognostic systems are constructed using signal processing techniques with real measured sensor acquired signals, to analyse and detect trends providing valuable evidence of system degradation [12,13]. Sensing modalities to acquire bearing degradation signatures that have been widely explored in recent years include vibration signals [1,3,14,15], acoustic emissions [16,17], stator current measurements [18][19][20], thermalimaging [21], and multiple sensor fusion [22,23]. Of these, vibration signals, acquired from mounted accelerometers is often attributed as the most favourable approach for conditionbased monitoring (CbM) in general, due to the non-invasive nature of the measurement data, low cost, robustness and ease of implementation in practice [24].…”

Section: Introductionmentioning

confidence: 99%

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Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

2023

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Improving the reliability and performance of electric and rotating machines is crucial to many industrial applications. This will lead to improved robustness, efficiency, and eco-sustainability, as well as mitigate significant health and safety concerns regarding sudden catastrophic failure modes. Bearing degradation is the most significant cause of machine failure and has been reported to cause up to 75% of low-voltage machine failures. This paper introduces a low complexity machine learning (ML) approach to estimate the remaining useful life (RUL) of rolling element bearings using real vibration signals. This work explores different ML recipes using novel feature engineering coupled with various k-Nearest Neighbour (k-NN), and Support Vector Machines (SVM) kernel and weighting functions in order to optimise this RUL approach. Original non-linear wear state models and feature sets are investigated, the latter are derived from Short-time Fourier Transform (STFT) and Hilbert Marginal Spectrum (HMS). These feature sets incorporate one-third octave band filtering for low complexity multivariate feature subspace compression. Our proposed ML algorithm stage has employed two robust supervised ML approaches: weighted k-NN and SVM. Real vibration data were drawn from the Pronostia platform to test and validate this prognostic monitoring approach. The results clearly demonstrate the effectiveness of this approach, with classification accuracy results of up to 82.8% achieved. This work contributes to the field by introducing a robust and computationally inexpensive method for accurate monitoring of machine health using low-cost vibration-based sensing.

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Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

2023

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“…However, the lack of models for identifying faults and estimating the energy costs associated with the level of technical condition does not allow the full use of the data collected [38][39][40]. The main purposes for using such data today include assessing performance degradation, constructing a performance index, and predicting the remaining equipment life [41][42][43][44]. However, the trend in industry and academia is to develop effective methods for the early detection of equipment failures, to decompose the factors influencing technical condition, and to identify the energy losses associated with technical condition [45][46][47][48][49].…”

Section: Introductionmentioning

confidence: 99%

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

2023

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Among the most widespread systems in industrial plants are automated drive systems, the key and most common element of which is the induction motor. In view of challenging operating conditions of equipment, the task of fault detection based on the analysis of electrical parameters is relevant. The authors propose the identification of patterns characterizing the occurrence and development of the bearing defect by the singular analysis method as applied to the stator current signature. As a result of the decomposition, the time series of the three-phase current are represented by singular triples ordered by decreasing contribution, which are reconstructed into the form of time series for subsequent analysis using a Hankelization of matrices. Experimental studies with bearing damage imitation made it possible to establish the relationship between the changes in the contribution of the reconstructed time series and the presence of different levels of bearing defects. By using the contribution level and tracking the movement of the specific time series, it became possible to observe both the appearance of new components in the current signal and the changes in the contribution of the components corresponding to the defect to the overall structure. The authors verified the clustering results based on a visual assessment of the component matrices’ structure similarity using scattergrams and hierarchical clustering. The reconstruction of the time series from the results of the component grouping allows the use of these components for the subsequent prediction of faults development in electric motors.

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“…The AI-based methods can deal with a large amount of monitoring data by machine learning (ML) techniques without much prior knowledge. Traditional machine learning techniques, such as Support Vector Machine (SVM) [16,17] and Relevance Vector Machine (RVM) [18], have been widely used in RUL prediction. Compared with the traditional machine learning techniques, deep learning methods show greater potential in dealing with high non-linearity and data complexity.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty-Controlled Remaining Useful Life Prediction of Bearings with a New Data-Augmentation Strategy

Wang

Yan

Shi³

et al. 2022

Applied Sciences

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The remaining useful life (RUL) of bearings based on deep learning methods has been increasingly used. However, there are still two obstacles in deep learning RUL prediction: (1) the training process of the deep learning model requires enough data, but run-to-failure data are limited in the actual industry; (2) the mutual dependence between RUL predictions at different time instants are commonly ignored in existing RUL prediction methods. To overcome these problems, a RUL prediction method combining the data augmentation strategy and Wiener–LSTM network is proposed. First, the Sobol sampling strategy is implemented to augment run-to-failure data based on the degradation model. Then, the Wiener–LSTM model is developed for the RUL prediction of bearings. Different from the existing LSTM-based bearing RUL methods, the Wiener–LSTM model utilizes the Wiener process to represent the mutual dependence between the predicted RUL results at different time instants and embeds the Wiener process into the LSTM to control the uncertainty of the result. A joint optimization strategy is applied in the construction of the loss function. The efficacy and superiority of the proposed method are verified on a rolling bearing dataset obtained from the PRONOSTIA platform. Compared with the conventional bearing RUL prediction methods, the proposed method can effectively augment the bearing run-to-failure data and, thus, improve the prediction results. Meanwhile, fluctuations of the bearing RUL prediction result are significantly suppressed by the proposed method, and the prediction errors of the proposed method are much lower than other comparative methods.

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Remaining Useful Life Estimation of Rotating Machines through Supervised Learning with Non-Linear Approaches

Cited by 6 publications

References 76 publications

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

Low Complexity Non-Linear Spectral Features and Wear State Models for Remaining Useful Life Estimation of Bearings

Induction Motor Bearing Fault Diagnosis Based on Singular Value Decomposition of the Stator Current

Uncertainty-Controlled Remaining Useful Life Prediction of Bearings with a New Data-Augmentation Strategy

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