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

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

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Entropy, as a complexity measure, has been widely applied for time series analysis. One preeminent example is the design of machine condition monitoring and industrial fault diagnostic systems. The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future data-driven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems

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

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Over the last few decades, the field of fault diagnostics and structural health management has been experiencing rapid developments. The reliability, availability, and safety of engineering systems can be significantly improved by implementing multifaceted strategies of in situ diagnostics and prognostics. With the development of intelligence algorithms, smart sensors, and advanced data collection and modeling techniques, this challenging research area has been receiving ever-increasing attention in both fundamental research and engineering applications. This has been strongly supported by the extensive applications ranging from aerospace, automotive, transport, manufacturing, and processing industries to defense and infrastructure industries

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

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 the wavelet packet decomposition. The entropy measure estimates the complexity 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 the synthetic and experimental signals for entropy-based feature extraction. In the proposed bearing fault diagnosis method, the F2CMPE is first used to extract the 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 the bearing fault patterns under different fault states and severity levels

Intelligent Biosignal Processing in Wearable and Implantable Sensors

This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition