Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio

A comparative study of WPD and EMD for shaft fault diagnosis

Fault diagnosis of incipient crack failure in rotating shafts allows the detection and identification of performance degradation as early as possible in industrial plants, such as downtime and potential injury to personnel. The present work studies the performance and effectiveness of crack fault detection by means of applying wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) on fault diagnosis of rotating shafts using multiscale entropy (MSE). After WPD and EMD, the most sensitive reconstruction vectors and intrinsic mode functions (IMFs) are selected using Shannon entropy. Then, these feature vectors are fed into support vector machine (SVM) for fault classification, where the entropy features represent the complexity of vibration signals with different scales. Experimental results have demonstrated that WPD combined with MSE can achieve an accuracy of 97.3% for crack fault detection in rotating shafts, whilst EMD combined with MSE has shown a higher detection rate of 98.5%

Fine-to-coarse multiscale permutation entropy for rolling bearing fault diagnosis

Multiscale Permutation Entropy (MPE) has been applied as a non-linear measure for estimating the complexity of time series. Nevertheless, the coarse-grained procedure in MPE only takes low-frequency information into account. To overcome this shortcoming, in this paper, a new entropy measure, named Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE), is proposed to provide stable and reliable results by offering both low-frequency and high-frequency information. Firstly, the F2C signals are created based on the reconstruction of selected wavelet coefficients using wavelet packet decomposition. Then, permutation entropy is used to estimate the complexity and dynamic change of the F2C signals. Experimental analysis is carried out to investigate and compare the performance of the proposed F2CMPE with that of the MPE. Results indicate that the proposed method can give consistent and stable entropy measure for rolling bearing fault diagnosis

A short survey on fault diagnosis of rotating machinery using entropy techniques

Fault diagnosis is significant for identifying latent abnormalities, and implementing fault-tolerant operations for minimizing performance degradation caused by failures in industrial systems, such as rotating machinery. The emergence of entropy theory contributes to precisely measure irregularity and complexity in a time series, which can be used for discriminating prominent fault information in rotating machinery. In this short paper, the utilization of entropy techniques for fault diagnosis of rotating machinery is summarized. Finally, open research trends and conclusions are discussed and presented respectively

Fault Diagnosis of Rotating Machinery using Improved Entropy Measures

Fault diagnosis of rotating machinery is of considerable significance to ensure high reliability and safety in industrial machinery. The key to fault diagnosis consists in detecting potential incipient fault presence, recognizing fault patterns, and identifying degrees of failures in machinery. The process of data-driven fault diagnosis method often requires extracting useful feature representations from measurements to make diagnostic decision-making. Entropy measures, as suitable non-linear complexity indicators, estimate dynamic changes in measurements directly, which are challenging to be quantified by conventional statistical indicators. Compared to single-scale entropy measures, multiple-scale entropy measures have been increasingly applied to time series complexity analysis by quantifying entropy values over a range of temporal scales. However, there exist a number of challenges in traditional multiple-scale entropy measures in analyzing bearing signals for bearing fault detection. Specifically, a large majority of multiple-scale entropy methods neglect high�frequency information in bearing vibration signal analysis. Moreover, the data length of transformed multiple signals is greatly reduced as scale factor increases, which can introduce incoherence and bias in entropy values. Lastly, non-linear and non-stationary behaviors of vibration signals due to interference and noise may reduce the diagnostic performance of traditional entropy methods in bearing health identification, especially in complex industrial settings. This dissertation proposes a novel multiple-scale entropy measure, named Adaptive Multiscale Weighted Permutation Entropy (AMWPE), for extracting fault features associated with complexity change in bearing vibration analysis. A new scale-extraction mechanism - adaptive Fine-to-Coarse (F2C) procedure - is presented to generate multiple-scale time series from the original signal. It has advantages of extracting low- and high-frequency information from measurements and generating improved multiple-scale time series with a hierarchical structure. Numerical evaluation is carried out to study the performance of the AMWPE measure in analyzing the complexity change of synthetic signals. Results demonstrated that the AMWPE algorithm could provide high consistency and stable entropy values in entropy estimation. It also presents high robustness against noise in analyzing noisy bearing signals in comparison with traditional entropy methods. Additionally, a new bearing diagnosis method is put forth, where the AMWPE method is applied for entropy analysis and a multi-class support vector machine classifier is used for identifying bearing fault patterns, respectively. Three experimental case studies are carried out to investigate the effectiveness of the proposed diagnosis method for bearing diagnosis. Comparative studies are presented to compare the diagnostic performance of the proposed entropy method and traditional entropy methods in terms of computational time of entropy estimation, feature representation, and diagnosis accuracy rate. Further, noisy bearing signals with different signal-to-noise ratios are analyzed using various entropy measures to study their robustness against noise in bearing diagnosis. Additionally, the developed adaptive F2C procedure can be extended to a variety of entropy algorithms based on improved single-scale entropy method used in entropy estimation. In the combination of artificial intelligence techniques, the improved entropy algorithms are expected to apply to machine health conditions and intelligent fault diagnosis in complex industrial machinery. Besides, they are suitable to evaluate the complexity and irregularity of other non-stationary signals measured from non-linear systems, such as acoustic emission signals and physiological signals

Crack detection in rotating shafts using wavelet analysis, Shannon entropy and multi-class SVM

Incipient fault diagnosis is essential to detect potential abnormalities and failures in industrial processes which contributes to the implementation of fault-tolerant operations for minimizing performance degradation. In this paper, an innovative method named Self-adaptive Entropy Wavelet (SEW) is proposed to detect incipient transverse crack faults on rotating shafts. Continuous Wavelet Transform (CWT) is applied to obtain optimized wavelet function using impulse modelling and decompose a signal into multi-scale wavelet coefficients. Dominant features are then extracted from those vectors using Shannon entropy, which can be used to discriminate fault information in different conditions of shafts. Support Vector Machine (SVM) is carried out to classify fault categories which identifies the severity of crack faults. After that, the effectiveness of this proposed approach is investigated in testing phrase by checking the consistency between testing samples with obtained model, the result of which has proved that this proposed approach can be effectively adopted for fault diagnosis of the occurrence of incipient crack failures on shafts in rotating machinery

A New Bearing Fault Diagnosis Method based on Fine-to-Coarse Multiscale Permutation Entropy, Laplacian Score and SVM

Fault diagnosis of rotating machinery is vital to identify incipient failures and avoid unexpected downtime in industrial systems. This paper proposes a new rolling bearing fault diagnosis method by integrating the Fine-to-Coarse Multiscale Permutation Entropy (F2CMPE), Laplacian Score (LS) and Support Vector Machine (SVM). A novel entropy measure, named F2CMPE, was proposed by calculating permutation entropy via multiple-scale fine-grained and coarse-grained signals based on wavelet packet decomposition. The entropy measure estimates the dynamic changes of time series from both low- and high-frequency components. Moreover, the F2CMPE mitigates the drawback of producing time series with sharply reduced data length via the coarse-grained procedure in the conventional Composite Multiscale Permutation Entropy (CMPE). The comparative performance of the F2CMPE and CMPE is investigated by analyzing synthetic and experimental signals for entropy-based feature extraction. In the proposed bearing fault diagnosis method, the F2CMPE is first used to extract entropy-based features from bearing vibration signals. Then, LS and SVM are used for selection of features and fault classification respectively. Finally, the effectiveness of the proposed method is verified for rolling bearing fault diagnosis using experimental vibration data sets, and the results have demonstrated the capability of the proposed method to recognize and identify bearing fault patterns under different fault states and severity levels

Self-adaptive fault diagnosis of roller bearings using infrared thermal images

Fault diagnosis of roller bearings in rotating machinery is of great significance to identify latent abnormalities and failures in industrial plants. This paper presents a new self-adaptive fault diagnosis system for different conditions of roller bearings using InfraRed Thermography (IRT). In the first stage of the proposed system, 2-Dimensional Discrete Wavelet Transform (2D-DWT) and Shannon entropy are applied respectively to decompose images and seek for the desired decomposition level of the approximation coefficients. After that, the histograms of selected coefficients are used as an input of the feature space selection method by using Genetic Algorithm (GA) and Nearest Neighbor (NN), for the purpose of selecting two salient features that can achieve the highest classification accuracy. The results have demonstrated that the proposed scheme can be employed effectively as an intelligent system for bearing fault diagnosis in rotating machinery

Cloud-based data-intensive framework towards fault diagnosis in large-scale petrochemical plants

Industrial Wireless Sensor Networks (IWSNs) are expected to offer promising monitoring solutions to meet the demands of monitoring applications for fault diagnosis in large-scale petrochemical plants, however, involves heterogeneity and Big Data problems due to large amounts of sensor data with high volume and velocity. Cloud Computing is an outstanding approach which provides a flexible platform to support the addressing of such heterogeneous and data-intensive problems with massive computing, storage, and data-based services. In this paper, we propose a Cloud-based Data-intensive Framework (CDF) for on-line equipment fault diagnosis system that facilitates the integration and processing of mass sensor data generated from Industrial Sensing Ecosystem (ISE). ISE enables data collection of interest with topic-specific industrial monitoring systems. Moreover, this approach contributes the establishment of on-line fault diagnosis monitoring system with sensor streaming computing and storage paradigms based on Hadoop as a key to the complex problems. Finally, we present a practical illustration referred to this framework serving equipment fault diagnosis systems with the ISE

Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures

Condition monitoring and incipient fault diagnosis of rolling bearing is of great importance to detect failures and ensure reliable operations in rotating machinery. In this paper, a new multi-speed fault diagnostic approach is presented by using self-adaptive wavelet transform components generated from bearing vibration signals. The proposed approach is capable of discriminating signatures from four conditions of rolling bearing, i.e. normal bearing and three different types of defected bearings on outer race, inner race and roller separately. Particle Swarm Optimization (PSO) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) based quasi-Newton minimization algorithms are applied to seek optimal parameters of Impulse Modelling based Continuous Wavelet Transform (IMCWT) model. Then, a three-dimensional feature space of the statistical parameters and a Nearest Neighbor (NN) classifier are respectively applied for fault signature extraction and fault classification. Effectiveness of this approach is then evaluated, and the results have achieved an overall accuracy of 100%. Moreover, the generated discriminatory fault signatures are suitable for multi-speed fault data sets. This technique will be further implemented and tested in a real industrial environment