PHM survey: implementation of signal processing methods for monitoring bearings and gearboxes

The reliability and safety of industrial equipments are one of the main objectives of companies to remain competitive in sectors that are more and more exigent in terms of cost and security. Thus, an unexpected shutdown can lead to physical injury as well as economic consequences. This paper aims to show the emergence of the Prognostics and Health Management (PHM) concept in the industry and to describe how it comes to complement the different maintenance strategies. It describes the benefits to be expected by the implementation of signal processing, diagnostic and prognostic methods in health-monitoring. More specifically, this paper provides a state of the art of existing signal processing techniques that can be used in the PHM strategy. This paper allows showing the diversity of possible techniques and choosing among them the one that will define a framework for industrials to monitor sensitive components like bearings and gearboxes

A Diagnosis Feature Space for Condition Monitoring and Fault Diagnosis of Ball Bearings

The problem of fault diagnosis and condition monitoring of ball bearings is a multidisciplinary subject. It involves research subjects from diverse disciplines of mechanical engineering, electrical engineering and in particular signal processing. In the first step, one should identify the correct method of investigation. The methods of investigation for condition monitoring of ball bearings include acoustic emission measurements, temperature monitoring, electrical current monitoring, debris analysis and vibration signal analysis. In this thesis the vibration signal analysis is employed. Once the method of analysis is selected, then features sensitive to faults should be calculated from the signal. While some of the features may be useful for condition monitoring, some of the calculated features might be extra and may not be helpful. Therefore, a feature reduction module should be employed. Initially, six features are selected as a candidate for the diagnosis feature space. After analyzing the trend of the features, it was concluded that three of the features are not appropriate for fault diagnosis. In this thesis, two problem is investigated. First the problem of identifying the effects of the fault size on the vibration signal is investigated. Also the performance of the feature space is tested in distinguishing the healthy ball bearings from the defective vibration signals

Optimal Wavelet Selection for the Size Estimation of Manufacturing Defects of Tapered Roller Bearings with Vibration Measurement using Shannon Entropy Criteria

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Wavelet support vector machine and multi-layer perceptron neural network with continues wavelet transform for fault diagnosis of gearboxes

In this paper, a method based on wavelet support vector machine (SVM) with OAOT algorithm, multi-layer perceptron (MLP) and Morlet wavelet transform were designed to diagnose different types of fault in a gearbox. A scale selection criterion based on the maximum relative energy to Shannon entropy ratio is proposed to determine optimal decomposition scale for wavelet analysis. Moreover, energy and entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. The results showed that the WSVM identified the fault categories of gearbox more accurately as compared to the MLP network

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

Exploiting Robust Multivariate Statistics and Data Driven Techniques for Prognosis and Health Management

This thesis explores state of the art robust multivariate statistical methods and data driven techniques to holistically perform prognostics and health management (PHM). This provides a means to enable the early detection, diagnosis and prognosis of future asset failures. In this thesis, the developed PHM methodology is applied to wind turbine drive train components, specifically focussed on planetary gearbox bearings and gears. A novel methodology for the identification of relevant time-domain statistical features based upon robust statistical process control charts is presented for high frequency bearing accelerometer data. In total, 28 time-domain statistical features were evaluated for their capabilities as leading indicators of degradation. The results of this analysis describe the extensible multivariate “Moments’ model” for the encapsulation of bearing operational behaviour. This is presented, enabling the early degradation of detection, predictive diagnostics and estimation of remaining useful life (RUL). Following this, an extended physics of failure model based upon low frequency SCADA data for the quantification of wind turbine gearbox condition is described. This extends the state of the art, whilst defining robust performance charts for quantifying component condition. Normalisation against loading of the turbine and transient states based upon empirical data is performed in the bivariate domain, with extensibility into the multivariate domain if necessary. Prognosis of asset condition is found to be possible with the assistance of artificial neural networks in order to provide business intelligence to the planning and scheduling of effective maintenance actions. These multivariate condition models are explored with multivariate distance and similarity metrics for to exploit traditional data mining techniques for tacit knowledge extraction, ensemble diagnosis and prognosis. Estimation of bearing remaining useful life is found to be possible, with the derived technique correlating strongly to bearing life (r = .96

Development of new fault detection methods for rotating machines (roller bearings)

Abstract Early fault diagnosis of roller bearings is extremely important for rotating machines, especially for high speed, automatic and precise machines. Many research efforts have been focused on fault diagnosis and detection of roller bearings, since they constitute one the most important elements of rotating machinery. In this study a combination method is proposed for early damage detection of roller bearing. Wavelet packet transform (WPT) is applied to the collected data for denoising and the resulting clean data are break-down into some elementary components called Intrinsic mode functions (IMFs) using Ensemble empirical mode decomposition (EEMD) method. The normalized energy of three first IMFs are used as input for Support vector machine (SVM) to recognize whether signals are sorting out from healthy or faulty bearings. Then, since there is no robust guide to determine amplitude of added noise in EEMD technique, a new Performance improved EEMD (PIEEMD) is proposed to determine the appropriate value of added noise. A novel feature extraction method is also proposed for detecting small size defect using Teager-Kaiser energy operator (TKEO). TKEO is applied to IMFs obtained to create new feature vectors as input data for one-class SVM. The results of applying the method to acceleration signals collected from an experimental bearing test rig demonstrated that the method can be successfully used for early damage detection of roller bearings. Most of the diagnostic methods that have been developed up to now can be applied for the case stationary working conditions only (constant speed and load). However, bearings often work at time-varying conditions such as wind turbine supporting bearings, mining excavator bearings, vehicles, robots and all processes with run-up and run-down transients. Damage identification for bearings working under non-stationary operating conditions, especially for early/small defects, requires the use of appropriate techniques, which are generally different from those used for the case of stationary conditions, in order to extract fault-sensitive features which are at the same time insensitive to operational condition variations. Some methods have been proposed for damage detection of bearings working under time-varying speed conditions. However, their application might increase the instrumentation cost because of providing a phase reference signal. Furthermore, some methods such as order tracking methods still can be applied when the speed variation is limited. In this study, a novel combined method based on cointegration is proposed for the development of fault features which are sensitive to the presence of defects while in the same time they are insensitive to changes in the operational conditions. It does not require any additional measurements and can identify defects even for considerable speed variations. The signals acquired during run-up condition are decomposed into IMFs using the performance improved EEMD method. Then, the cointegration method is applied to the intrinsic mode functions to extract stationary residuals. The feature vectors are created by applying the Teager-Kaiser energy operator to the obtained stationary residuals. Finally, the feature vectors of the healthy bearing signals are utilized to construct a separating hyperplane using one-class support vector machine. Eventually the proposed method was applied to vibration signals measured on an experimental bearing test rig. The results verified that the method can successfully distinguish between healthy and faulty bearings even if the shaft speed changes dramatically

선형 시간-주파수 표현에서 모터와 기어박스의 고장 특성 감지를 위한 가중 잔차 레니 정보에 관한 연구

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 윤병동.Many studies have been conducted for fault detection of rotating machinery under varying speed conditions using time-frequency representation (TFR). However, the parameters of TFR have been selected by researchers empirically in most previous studies. Also, the previously proposed TFR measures do not suggest the optimal parameter for fault diagnosis. This paper thus proposed a TFR measure to select the parameter from the perspective of detecting fault features. The proposed measure, Weighted Residual Rényi Information (WRRI), is based on Rényi Information, selected through a comparative study among previously suggested measures. WRRI, defined as a modified form of the input atom of Rényi Information, consists of two terms. The first term is the residual term that extracts the fault feature, and the second term is the weighting term that reduces the effect of noise. The validation process consists of the two steps; 1) analytic signal, 2) motor, and gearbox signal. In the validation using an analytic signal, it confirmed that WRRI suggested a better parameter for detecting fault features than the Rényi Information. Also, in the validation using a motor testbed signal and gearbox testbed signal, it confirmed that WRRI was possible to select more suitable parameters for fault diagnosis than the Rényi Information.변속 조건에서 운전되는 회전기기 고장진단을 위해 시간-주파수 표현을 사용한 많은 연구들이 수행되어왔다. 하지만 대부분의 연구에서 시간-주파수 표현의 파라미터는 연구자들에 의해 경험적으로 선택되었다. 또한 이전에 제안된 시간-주파수 표현 측정방법도 고장 진단을 위한 최적의 파라미터를 제안해주지 못한다. 본 연구에서는 고장 특징 검출을 목적으로 시간-주파수 표현의 파라미터를 제안해주는 측정방법을 제안한다. 제안 측정방법 가중 잔차 레니 정보(WRRI)는 이전 연구들에서 제안된 측정밥법들에 대한 비교연구를 통해 선정된 레니 정보에 기반한다. WRRI는 레니 정보의 입력 형태를 2가지 성분으로 구성된 변형 형태를 통해 정의된다. 첫 번째 성분은 고장 특징 추출을 위한 잔차성분이고, 두 번째 성분은 노이즈의 영향성을 줄이기 위한 가중성분이다. 검증 과정은 산술적 신호와 모터, 기어 박스로 이루어진 신호를 통해 2 단계로 진행된다. 산술적 신호를 사용한 검증과정에서 WRRI는 기존 측정 방법인 레니 정보보다 고장 특징 검출에 더 적합한 시간-주파수 표현 파라미터를 제안했다. 또한 모터와 기어박스 테스트베드 신호를 사용한 검증과정에서 WRRI는 레니 정보보다 고장 특징 추출과 진단에 더 적합한 시간-주파수 파라미터를 제안했다.Chapter 1 . Introduction 1 1.1 Introduction 1 Chapter 2 . TFR Measure for Readability 4 2.1 Linear TFR 4 2.2 TFR Measures 11 2.3 Comparative Study of Previous Measure 13 Chapter 3 . TFR Measure for Detectability 16 3.1 Fault Feature Detectability 16 3.2 Weighted Residual Rnyi Information 22 Chapter 4 . Validation of the Proposed Measure 29 4.1 Analytic Signals Having Fault Feature 29 4.2 Experiment Signal 33 Chapter 5 . Conclusion 57 Bibliography 58 국문 초록 64Maste

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