Fault detection in rotating machinery using acoustic emission

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonRotating machinery is a critical asset of industrial plants worldwide. Bearings and gearboxes are two of the most common components found in rotating machinery of industrial plants. The malfunction of bearings and gearboxes lead the machine to fail and often these failures occur catastrophically leading to personnel injuries. Therefore it is of high importance to identify the deterioration at an early stage. Among the techniques applied to detect damage in rotating machinery, acoustic emission has been a prevalent field of research for its potential to detect defects at an earlier stage than other more established techniques such as vibration analysis and oil analysis. However, to reliably detect the fault at an early stage de-noising techniques often must be applied to reduce the AE noise generated by neighbouring components and normal component operation. For this purpose a novel signal processing algorithm has been developed combining Wavelet Packets as a pre-processor, Hilbert Transform, Autocorrelation function and Fast Fourier transform. The combination of these techniques allows identification of g repetitive patterns in the AE signal that are attributable to bearing and gear damage. The enhancement for early stage defect detection in bearings and gears provided by this method is beneficial in planning maintenance in advance, reducing machinery down-time and consequently reducing the costs associated with bearing breakdown. The effectiveness of the proposed method has been investigated experimentally using seeded and naturally developed defects in gears and bearings. In addition, research into the optimal Wavelet Packet node that offers the best de-noising results has been performed showing that the 250-750 kHz band gives the best SNR results. The detection of shaft angular misalignment using Acoustic Emission has been investigated and compared with acceleration spectra. The results obtained show enhancements of AE in detection shaft angular misalignment over vibration analysis in SNR and stability with varying operational conditions

Condition Monitoring and Fault Diagnosis of Roller Element Bearing

Rolling element bearings play a crucial role in determining the overall health condition of a rotating machine. An effective condition-monitoring program on bearing operation can improve a machine’s operation efficiency, reduce the maintenance/replacement cost, and prolong the useful lifespan of a machine. This chapter presents a general overview of various condition-monitoring and fault diagnosis techniques for rolling element bearings in the current practice and discusses the pros and cons of each technique. The techniques introduced in the chapter include data acquisition techniques, major parameters used for bearing condition monitoring, signal analysis techniques, and bearing fault diagnosis techniques using either statistical features or artificial intelligent tools. Several case studies are also presented in the chapter to exemplify the application of these techniques in the data analysis as well as bearing fault diagnosis and pattern recognition

Signal processing techniques for machine condition monitoring

The purpose of this dissertation is to analyse and compare a wide range of wavelet de-noising parameters and determine which parameters are best suited to the de-noising of rolling element bearing vibration signals. The condition of rolling element bearings is often monitored by recording the vibration of the bearing using accelerometers and analysing the signal for particular frequency content. When a bearing experiences a mechanical fault the vibration signal will contain frequencies relating to the failing component. Monitoring the bearing vibration can provide advanced warning that a bearing failure is imminent. However, when a mechanical fault is in the early stages of development the fault frequency can be very low in magnitude and difficult to detect. Improving the signal to noise ratio of these fault frequencies can provide earlier detection of the fault. One method to improve the signal to noise ratio of a bearing fault frequency is to reduce the noise component in the vibration signal using wavelet theory. Wavelet de-noising has many parameters that can be varied which changes how the de-noising process modifies the vibration signal in the time domain. This dissertation makes comparison between the many de-noising parameters available and assesses which parameters provide the best increase in signal to noise ratio. The wavelet de-noising process alone does not identify the frequencies relating to a bearing fault. The frequency content within the vibration time domain signal is required to be extracted and assessed to determine the effect of the wavelet de-noising process. Three frequency extraction methods were used to analyse the de-noised signals and indicate the magnitude of signal to noise ratio improvement achieved through de-noising. This dissertation shows that cepstrum analysis of the time domain signal responded best to the wavelet de-noising process and large improvements in signal to noise ratio were realised

Use of the continuous wavelet tranform to enhance early diagnosis of incipient faults in rotating element bearings

This thesis focused on developing a new wavelet for use with the continuous wavelet transform, a new detection method and two de-noising algorithms for rolling element bearing fault signals. The work is based on the continuous wavelet transform and implements a unique Fourier Series estimation algorithm that allows for least squares estimation of arbitrary frequency components of a signal. The final results of the research also included use of the developed detection algorithm for a novel method of estimating the center frequency and bandwidth for use with the industry standard detection algorithm, envelope demodulation, based on actual fault data. Finally, the algorithms and wavelets developed in this paper were tested against seven other wavelet based de-noising algorithms and shown to be superior for the de-noising and detection of inner and outer rolling element race faults

An Assessment on the Non-Invasive Methods for Condition Monitoring of Induction Motors

The ability to forecast motor mechanical faults at incipient stages is vital to reducing maintenance costs, operation downtime and safety hazards. This paper synthesized the progress in the research and development in condition monitoring and fault diagnosis of induction motors. The motor condition monitoring techniques are mainly classified into two categories that are invasive and non-invasive techniques. The invasive techniques are very basic, but they have some implementation difficulties and high cost. The non-invasive methods, namely MCSA, PVA and IPA, overcome the disadvantages associated to invasive methods. This book chapter reviews the various non-invasive condition monitoring methods for diagnosis of mechanical faults in induction motor and concludes that the instantaneous power analysis (IPA) and Park vector analysis (PVA) methods are best suitable for the diagnosis of small fault signatures associated to mechanical faults. Recommendations for the future research in these areas are also presented

Acoustic spectral imaging and transfer learning for reliable bearing fault diagnosis under variable speed conditions.

Incipient fault diagnosis of a bearing requires robust feature representation for an accurate condition-based monitoring system. Existing fault diagnosis schemes are mostly confined to manual features and traditional machine learning approaches such as artificial neural networks (ANN) and support vector machines (SVM). These handcrafted features require substantial human expertise and domain knowledge. In addition, these feature characteristics vary with the bearing's rotational speed. Thus, such methods do not yield the best results under variable speed conditions. To address this issue, this paper presents a reliable fault diagnosis scheme based on acoustic spectral imaging (ASI) of acoustic emission (AE) signals as a precise health state. These health states are further utilized with transfer learning, which is a machine learning technique, which shares knowledge with convolutional neural networks (CNN) for accurate diagnosis under variable operating conditions. In ASI, the amplitudes of the spectral components of the windowed time-domain acoustic emission signal are transformed into spectrum imaging. ASI provides a visual representation of acoustic emission spectral features in images. This ensures enhanced spectral images for transfer learning (TL) testing and training, and thus provides a robust classifier technique with high diagnostic accuracy

진동 생성 메커니즘을 고려한 초기 결함 단계의 베어링 진단 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 윤병동.The failure of rolling element bearings is a common fault in rotating machines. These failures can have catastrophic consequences, including fatal injuries and significant financial losses. To mitigate these risks, researchers have explored various ways to detect and prevent bearing failures as early as possible. One promising approach is the use of condition monitoring data; in this approach, vibration data has been found to be particularly effective for identifying and preventing potential failures. However, the use of vibration signals to diagnose bearings at the incipient fault stage is a challenging task, in part due to the gap between the controlled conditions under which research data is often generated and the actual field conditions in which these bearings operate. In particular, fault-related signals are weak and nonstationary; further, they are usually obscured by noise that arises from environmental factors. Additionally, these signals may be complicated or modulated, making them difficult to discern. To properly address these research issues, this dissertation aims at advancing two research thrusts focused on developing techniques for modeling and analyzing vibration signals based on physical phenomena. In Research Thrust 1, a quasi-periodic impulse train model with an impact force function is suggested to brtidge the gap between theory and reality. In this research, a pseudo second-order cyclostationary signal is modeled using the quasi-periodic impulse train model. In order to simulate the dynamic response of a system, considering the physical behaviors in bearings, the impact force function that reflects the change in contact stress is used. Finally, the proposed model is validated by performing signal processing on the synthesized signal, including simulation of the proposed model. The result confirm that an appropriate preprocessing process is essential to diagnose bearing failure at the incipient failure stage, further, that finding the frequency band that contains the failure information is essential for performance improvement. In Research Thrust 2, a new feature extraction method is proposed for bearing diagnosis using vibration signals, namely the linear power normalized cepstral coefficients (LPNCC). The proposed approach is designed to enhance the bearing signal, which is buried in noise that arises from environmental effects, and which contains mechanical phenomena. The proposed method consists of two sequentially executed steps: 1) extraction of the LPNCC and 2) demodulation analysis that is performed by examining the squared envelope spectra (SES). Combined, this approach is called LPNCC-SES. The performance of the proposed method is examined by applying it to both simulation data and experimental cases. The results show a high level of accuracy and robustness in the diagnostic capabilities of the method, making it suitable for use in maintenance and diagnostic routines.구름 베어링은 회전 기계 및 왕복동 기계의 핵심적인 요소부품으로 회전하거나 진동하는 구조를 지지하며 구성품 간의 하중을 전달한다. 따라서 구름 베어링의 고장은 시스템 전체의 고장으로 이어져 치명적인 인명 피해는 물론 막대한 재정적 손실을 초래할 수 있다. 이에 따라 조기에 베어링의 고장을 관측하고 진단하기 위해 상태관측 데이터를 활용한 많은 연구가 진행되어 왔으며 특히 진동신호를 활용한 진단이 널리 수행되었다. 베어링 초기 결함을 진단을 하는데 있어 어려움을 겪게 하는 이유로 환경 영향으로 인해 발생하는 잡음에 묻혀 있는 약한 결함 신호 및 베어링의 결함 관련 신호의 복잡한 변조를 들 수있다. 이러한 문제를 극복하기 위해 본 연구에서는 베어링 결함신호의 생성원리에에 기반한 신호 모델을 제안하였다. 베어링 신호는 본질적으로 비정상성을 띄며 또한 실제 현장에서 획득한 신호는 복잡하고 다양한 소스에서 발생하는 신호가 조합된다. 이론과 현실 사이의 격차를 해소하기 위해 해석적 신호 모델에 헤르츠 접촉 이론에 기반한 충격 메커니즘을 구현하였다. 시뮬레이션된 베어링 신호에 기어의 결정론적 신호, 회전축의 사인파 신호 및 가우시안 노이즈와 합성된 신호에 대한 전처리 분석을 통해 제안 모델의 타당성을 검증하였다. 이 후, 다양한 잡음 환경에서 여러 변조된 음성 신호를 효과적으로 판별하는 음성인식 방법을 기계시스템에 적용한 고장특징 추출 방법을 새로이 제안하여 캡스트럽에 기반한 특징인자를 추출하였다. 추출된 인자로부터 시간-주파수 영역에서 스펙트럼을 계산하여 효과적으로 베어링의 특성 주파수를 검출하였다. 제안된 방법의 검증을 위해 다양한 잡음 환경에서의 시뮬레이션 데이터와 실험데이터를 사용하였다. 또한 가속수명시험을 통한 데이터를 통하여 조기진단의 효과를 검증하였다.Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 3 1.3 Dissertation Layout 5 Chapter 2. Technical Background and Literature Review 6 2.1 Vibration Signals of Bearing Faults 6 2.1.1 Rolling Element Bearings 6 2.1.2 Failure of Rolling Element Bearings 7 2.1.3 Bearing Fault Signature and Its Frequencies 8 2.2 Vibration Techniques for Bearing Incipient Fault Diagnosis 10 2.2.1 Overview of Vibration Techniques for Bearings 10 2.2.2 Cepstrum-Based Fault Diagnosis Techniques 13 Chapter 3. Quasi Periodic Impulse Train Model with Impact Force Function 20 3.1 Vibration Modelling of Bearing Fault 21 3.1.1 General Mathematical Model 21 3.1.2 Quasi-periodic Model with Cyclostationary 22 3.1.3 Excitation Force Function in Dynamic Models 23 3.2 Quasi Period Impulse Model with Impact Function 26 3.2.1 Overall Process of Proposed Model 26 3.2.2 Modeling the Excitation Force 27 3.3 Numerical Results and Discussion 32 3.3.1 Necessity of Choosing an Appropriate Preprocessing Method 34 Chapter 4. Speech Recognition-Inspired Feature Engineering for Bearing Fault Diagnosis 48 4.1 Review of Power-Normalized Cepstral Coefficients (PNCC) 49 4.1.1 Basic Definition of Cepstrum 49 4.1.2 Characteristics of cepstrum in mechanical vibrations 50 4.1.3 Power-Normalized Cepstral Coefficients (PNCC) 52 4.2 Proposed Feature Extraction Method: Linear Power-Normalized Cepstral Coefficients (LPNCC) 55 4.3 Fault Diagnosis by Implementing LPNCC 57 4.3.1 Fault Diagnosis Method using LPNCC and Squared Envelope Spectrum (LPNCC-SES) 57 4.3.2 Effect of Linear Filter and Power-normalization 59 4.4 Experimental Application and Results 60 4.4.1 Case Study with Simulation Model 61 4.4.1.1. Simulation Data with White Gaussian Noise 61 4.4.1.2. Denoising Under Gaussian Noise 62 4.4.1.3. Reseults Under Non-gaussian Noise 66 4.4.2 Case Study with Experiment Data 67 4.4.2.1. Experimental Data: Case Western Reserve University Dataset 67 4.4.2.1.1. Compared Methods 67 4.4.2.1.2. Case 1: Impusive Noise 68 4.4.2.1.3. Case 2: Low Signal-to-noise Ratio (SNR) 69 4.4.2.1.4. Case 3: Multiple Defective Signals 71 4.4.2.2. Experimental Data: Naturally Degradation Data 72 Chapter 5. Conclusions 108 5.1 Summary of Dissertation 108 5.2 Contributions and Significance 110 5.3 Suggestions for Future Research 113 References 116 Abstract (Korean) 130박

Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings : a review

A rolling bearing is an essential component of a rotating mechanical transmission system. Its performance and quality directly affects the life and reliability of machinery. Bearings’ performance and reliability need high requirements because of a more complex and poor working conditions of bearings. A bearing with high reliability reduces equipment operation accidents and equipment maintenance costs and achieves condition-based maintenance. First in this paper, the development of technology of the main individual physical condition monitoring and fault diagnosis of rolling bearings are introduced, then the fault diagnosis technology of multi-sensors information fusion is introduced, and finally, the advantages, disadvantages, and trends developed in the future of the detection main individual physics technology and multi-sensors information fusion technology are summarized. This paper is expected to provide the necessary basis for the follow-up study of the fault diagnosis of rolling bearings and a foundational knowledge for researchers about rolling bearings.The Natural Science Foundation of China (NSFC) (grant numbers: 51675403, 51275381 and 51505475), National Research Foundation, South Africa (grant numbers: IFR160118156967 and RDYR160404161474), and UOW Vice-Chancellor’s Postdoctoral Research Fellowship.International Journal of Advanced Manufacturing Technology2019-04-01hj2018Electrical, Electronic and Computer Engineerin