Zero-Shot Motor Health Monitoring by Blind Domain Transition

Continuous long-term monitoring of motor health is crucial for the early detection of abnormalities such as bearing faults (up to 51% of motor failures are attributed to bearing faults). Despite numerous methodologies proposed for bearing fault detection, most of them require normal (healthy) and abnormal (faulty) data for training. Even with the recent deep learning (DL) methodologies trained on the labeled data from the same machine, the classification accuracy significantly deteriorates when one or few conditions are altered. Furthermore, their performance suffers significantly or may entirely fail when they are tested on another machine with entirely different healthy and faulty signal patterns. To address this need, in this pilot study, we propose a zero-shot bearing fault detection method that can detect any fault on a new (target) machine regardless of the working conditions, sensor parameters, or fault characteristics. To accomplish this objective, a 1D Operational Generative Adversarial Network (Op-GAN) first characterizes the transition between normal and fault vibration signals of (a) source machine(s) under various conditions, sensor parameters, and fault types. Then for a target machine, the potential faulty signals can be generated, and over its actual healthy and synthesized faulty signals, a compact, and lightweight 1D Self-ONN fault detector can then be trained to detect the real faulty condition in real time whenever it occurs. To validate the proposed approach, a new benchmark dataset is created using two different motors working under different conditions and sensor locations. Experimental results demonstrate that this novel approach can accurately detect any bearing fault achieving an average recall rate of around 89% and 95% on two target machines regardless of its type, severity, and location.Comment: 13 pages, 9 figures, Journa

Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis

© 2017 IEEE. Personal use of this material is permitted. Permissíon from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertisíng or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] With the expansion of the use of electrical drive sys- tems to more critical applications, the issue of reliability and fault mitigation and condition-based maintenance have consequently taken an increasing importance: it has become a crucial one that cannot be neglected or dealt with in an ad-hoc way. As a result research activity has increased in this area, and new methods are used, some based on a continuation and improvement of previous accomplishments, while others are applying theory and techniques in related areas. This Special Section of the IEEE Transactions on Industrial Informatics attracted a number of papers dealing with Advanced Signal and Image Processing Techniques for Electric Machine and Drives Fault Diagnosis and Prognosis. This editorial aims to put these contributions in context, and highlight the new ideas and directions therein.Antonino-Daviu, J.; Lee, SB.; Strangas, E. (2017). Guest Editorial Special Section on Advanced Signal and Image Processing Techniques for Electric Machines and Drives Fault Diagnosis and Prognosis. IEEE Transactions on Industrial Informatics. 13(3):1257-1260. doi:10.1109/TII.2017.2690464S1257126013

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 fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning.

Centrifugal pumps are the most vital part of any process industry. A fault in centrifugal pump can affect imperative industrial processes. To ensure reliable operation of the centrifugal pump, this paper proposes a novel automated health state diagnosis framework for centrifugal pump that combines a signal to time-frequency imaging technique and an Adaptive Deep Convolution Neural Network model (ADCNN). First, the vibration signals corresponding to different health conditions of the centrifugal pump are acquired. Vibration signals obtained from the centrifugal pump carry a great deal of information and generally, statistical features are extracted from the vibration signals to retain meaningful fault information. However, these features are either insensitive to weak incipient faults or unsuitable for tracking severe faults, thus, decreasing the fault classification accuracy. To tackle this problem, a signal to time-frequency imaging technique is applied to the pump vibration signals. For this purpose, Continuous Wavelet Transform (CWT) is applied to decompose the vibration signals over different time-frequency scales and extract the pump fault information in both the time and frequency domains. The CWT scales form two-dimensional time-frequency images commonly referred to as scalograms. The CWT scalograms are then converted into grayscale images (SGI). Over the past few decades, CNN models have been established as an effective practice to process images for classification and pattern recognition. Consequently, the extracted CWTSGIs are finally provided as inputs to the proposed ADCNN architecture to achieve feature extraction and classification for centrifugal pump faults. The performance of the proposed diagnostic framework (CWTSGI + ADCNN) is validated with a vibration dataset collected from a testbed specifically designed for centrifugal pump diagnosis. The experimental results suggest that the proposed technique based on CWTSGI and ADCNN outperformed existing methods with an average performance improvement of 4.7 - 15.6%

딥러닝 기반 음향 이상 강도 추정

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 기계항공공학부, 2019. 2. 윤병동.이 연구는 극단적인 정상과 이상 음향 신호만을 학습하여, 임의의 음향 신호의 이상 정도를 추정할 수 있는 딥러닝 알고리즘 기반 방법론에 대한 것이다. 우선 연속적으로 강도가 변화하는 이상 음향을 구현하기 위해 두 종류의 이상 신호를 실험적으로 합성하였다. 정상과 심한 이상 음향을 스펙트로그램으로 변환하여 다른 데이터로 이미 가중치가 학습된 CNN 모델로 분류를 시도한 결과, 아주 높은 수준의 정확도로 분류가 가능한 것이 확인되었다. 그러나 이 과정에서 학습된 모델로도 중간 정도의 이상 음향을 구분해낼 수 없었다. 이 한계점을 극복하기 위해서 우리는 잠재 공간의 특징 인자를 추출하였다. 우리는 특징 인자의 차원을 축소한 결과, 이상 정도의 증가에 따라 차원 축소된 인자 값이 서서히 변하는 현상을 관찰하였다. 이 현상은 정상 상태와 이상 상태의 특징 인자 군집 사이에 중간 정도의 이상을 가진 음향의 특징 인자를 위치시킬 수 있음을 시사한다. 마지막으로 이 방법론은 비음향 진동 데이터를 포함한 실제 환경에서 계측된 데이터들에 적용되었다. 제시된 방법론은 실제 데이터에 대해서도 유의미한 결과를 보였으며, 시주파수 영역 상에서 상태가 변화하는 이상에 공통적으로 적용될 수 있음을 제시하였다.This research proposes a deep learning-based method to estimate an intermediate severity fault state of acoustic data using a model trained only with normal and severe fault labels. First, two types of synthesized acoustic faults with five parameters were designed to simulate a gradually increasing fault. Then, a pretrained CNN model was applied to spectrogram images built from the data. The results from this model prove that classification of both normal and severe faults is possible with high accuracy. However, distinguishing intermediate faults was not possible, even with a fine-tuned model of highest accuracy. To overcome this limitation, latent space features were extracted using the model. Based on this information, the feature values were shown to gradually change as the severity of the fault increased in the reduced-dimension space. This phenomenon suggests that it is possible to map data with intermediate-level faults in the space somewhere between normal and severe fault clusters. The method was tested on real data, including non-acoustic vibrational data. It is anticipated that the proposed method can be applied not only to acoustic signals but also to any signals with a fault characteristic that gradually changes in the time-frequency domain as the fault propagates.Table of Contents Abstract i List of Tables vi List of Figures vii Chapter 1. Introduction 1 1.1 Motivation 1 1.2 Scope of the Research 3 1.3 Thesis Layout 6 Chapter 2. Research Background 7 2.1 Types of Acoustic Faults 7 2.2 Spectrogram 8 2.3 CNN Models 10 2.3.1 VGG-16 and VGG-19 11 2.3.2 ResNet-50 12 2.3.3 InceptionV3 13 2.3.4 Xception 15 2.4 Transfer Learning 16 2.5 Latent Space 17 2.5.1 Latent Space Visualization 17 Chapter 3. Proposed Estimation Method 18 3.1 Simulating Acoustic Fault 18 3.1.1 Modulation Fault 21 3.1.2 Impulsive Fault 22 3.2 Spectrogram Parameters 23 3.3 Transfer Learning and Fine-tuning 25 3.4 Latent Space Visualization 26 Chapter 4. Experiment Result 27 4.1 Synthesized Data 27 4.1.1 Transfer Learning Result 27 4.1.2 Prediction Result 28 4.1.3 Latent Space Visualization Result 32 4.2 Case Western Reserve University Bearing Dataset 35 4.2.1 Latent Space Visualization Result 36 4.3 Unbalanced Fan Data 37 4.3.1 Latent Space Visualization Result 37 Chapter 5. Conclusion and Future Work 39 5.1 Conclusion 39 5.2 Contribution 40 5.3 Future Work 41Maste

Intelligent Feature Extraction, Data Fusion and Detection of Concrete Bridge Cracks: Current Development and Challenges

As a common appearance defect of concrete bridges, cracks are important indices for bridge structure health assessment. Although there has been much research on crack identification, research on the evolution mechanism of bridge cracks is still far from practical applications. In this paper, the state-of-the-art research on intelligent theories and methodologies for intelligent feature extraction, data fusion and crack detection based on data-driven approaches is comprehensively reviewed. The research is discussed from three aspects: the feature extraction level of the multimodal parameters of bridge cracks, the description level and the diagnosis level of the bridge crack damage states. We focus on previous research concerning the quantitative characterization problems of multimodal parameters of bridge cracks and their implementation in crack identification, while highlighting some of their major drawbacks. In addition, the current challenges and potential future research directions are discussed.Comment: Published at Intelligence & Robotics; Its copyright belongs to author

A New Method for Friction Estimation in EMA Transmissions

The increasing interest for adopting electromechanical actuators (EMAs) on aircraft demands improved diagnostic and prognostic methodologies to be applied to such systems in order to guarantee acceptable levels of reliability and safety. While diagnostics methods and techniques can help prevent fault propagation and performance degradation, prognostic methods can be applied in tandem to reduce maintenance costs and increase overall safety by enabling predictive and condition-based maintenance schedules. In this work, a predictive approach for EMAs friction torque estimation is proposed. The algorithm is based on the reconstruction of the residual torque in mechanical transmissions. The quantity is then sampled and an artificial neural network (ANN) is used to obtain an estimation of the current health status of the transmission. Early results demonstrate that such an approach can predict the transmission health status with good accuracy

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

Artificial Intelligence-based Technique for Fault Detection and Diagnosis of EV Motors: A Review

The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed