Multidimensional prognostics for rotating machinery: A review

open access articleDetermining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and avail- ability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidi- mensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and draw- backs and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Monitoring the misalignment of machine tools with autoencoders after they are trained with transfer learning data

CNC machines have revolutionized manufacturing by enabling high-quality and high-productivity production. Monitoring the condition of these machines during production would reduce maintenance cost and avoid manufacturing defective parts. Misalignment of the linear tables in CNCs can directly affect the quality of the manufactured parts, and the components of the linear tables wear out over time due to the heavy and fluctuating loads. To address these challenges, an intelligent monitoring system was developed to identify normal operation and misalignments. Since damaging a CNC machine for data collection is too expensive, transfer learning was used in two steps. First, a specially designed experimental feed axis test platform (FATP) was used to sample the current signal at normal and five levels of left-side misalignment conditions ranging from 0.05 to 0.25 mm. Four different algorithm combinations were trained to detect misalignments. These combinations included a 1D convolution neural network (CNN) and autoencoder (AE) combination, a temporal convolutional network (TCN) and AE combination, a long short-term memory neural network (LSTM) and AE combination, and a CNN, LSTM, and AE combination. At the second step, Wasserstein deep convolutional generative adversarial network (W-DCGAN) was used to generate data by integrating the observed characteristics of the FATP at different misalignment levels and collected limited data from the actual CNC machines. To evaluate the similarity and limited diversity of generated and real signals, t-distributed stochastic neighbor embedding (T-SNE) method was used. The hyperparameters of the model were optimized by random and grid search. The CNN, LSTM, and AE combination demonstrated the best performance, which provides a practical way to detect misalignments without stopping production or cluttering the work area with sensors. The proposed intelligent monitoring system can detect misalignments of the linear tables of CNCs, thus enhancing the quality of manufactured parts and reducing production costs

Similarity-based methods for machine diagnosis

This work presents a data-driven condition-based maintenance system based on similarity-based modeling (SBM) for automatic machinery fault diagnosis. The proposed system provides information about the equipment current state (degree of anomaly), and returns a set of exemplars that can be employed to describe the current state in a sparse fashion, which can be examined by the operator to assess a decision to be made. The system is modular and data-agnostic, enabling its use in different equipment and data sources with small modifications. The main contributions of this work are: the extensive study of the proposition and use of multiclass SBM on different databases, either as a stand-alone classification method or in combination with an off-the-shelf classifier; novel methods for selecting prototypes for the SBM models; the use of new similarity functions; and a new production-ready fault detection service. These contributions achieved the goal of increasing the SBM models performance in a fault classification scenario while reducing its computational complexity. The proposed system was evaluated in three different databases, achieving higher or similar performance when compared with previous works on the same database. Comparisons with other methods are shown for the recently developed Machinery Fault Database (MaFaulDa) and for the Case Western Reserve University (CWRU) bearing database. The proposed techniques increase the generalization power of the similarity model and of the associated classifier, having accuracies of 98.5% on MaFaulDa and 98.9% on CWRU database. These results indicate that the proposed approach based on SBM is worth further investigation.Este trabalho apresenta um sistema de manutenção preditiva para diagnóstico automático de falhas em máquinas. O sistema proposto, baseado em uma técnica denominada similarity-based modeling (SBM), provê informações sobre o estado atual do equipamento (grau de anomalia), e retorna um conjunto de amostras representativas que pode ser utilizado para descrever o estado atual de forma esparsa, permitindo a um operador avaliar a melhor decisão a ser tomada. O sistema é modular e agnóstico aos dados, permitindo que seja utilizado em variados equipamentos e dados com pequenas modificações. As principais contribuições deste trabalho são: o estudo abrangente da proposta do classificador SBM multi-classe e o seu uso em diferentes bases de dados, seja como um classificador ou auxiliando outros classificadores comumente usados; novos métodos para a seleção de amostras representativas para os modelos SBM; o uso de novas funções de similaridade; e um serviço de detecção de falhas pronto para ser utilizado em produção. Essas contribuições atingiram o objetivo de melhorar o desempenho dos modelos SBM em cenários de classificação de falhas e reduziram sua complexidade computacional. O sistema proposto foi avaliado em três bases de dados, atingindo desempenho igual ou superior ao desempenho de trabalhos anteriores nas mesmas bases. Comparações com outros métodos são apresentadas para a recém-desenvolvida Machinery Fault Database (MaFaulDa) e para a base de dados da Case Western Reserve University (CWRU). As técnicas propostas melhoraram a capacidade de generalização dos modelos de similaridade e do classificador final, atingindo acurácias de 98.5% na MaFaulDa e 98.9% na base de dados CWRU. Esses resultados apontam que a abordagem proposta baseada na técnica SBM tem potencial para ser investigada em mais profundidade

Development of a Methodology for Condition-Based Maintenance in a Large-Scale Application Field

This paper describes a methodology, developed by the authors, for condition monitoring and diagnostics of several critical components in the large-scale applications with machines. For industry, the main target of condition monitoring is to prevent the machine stopping suddenly and thus avoid economic losses due to lack of production. Once the target is reached at a local level, usually through an R&D project, the extension to a large-scale market gives rise to new goals, such as low computational costs for analysis, easily interpretable results by local technicians, collection of data from worldwide machine installations, and the development of historical datasets to improve methodology, etc. This paper details an approach to condition monitoring, developed together with a multinational corporation, that covers all the critical points mentioned above

회전기계 내 저해상도 및 고해상도 신호를 활용한 딥러닝 기반 거시적 및 미시적 고장 진단 방법론

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2023. 2. 윤병동.Rotating machinery is widely used in many industrial sites, including manufacturing and power generation. Unpredicted failures in these systems can result in huge economic and human losses. To prevent this situation, fault diagnosis studies have gathered much attention, with the goal of operating rotating machines without the occurrence of any unpredicted problems. Fault diagnosis methods aim to accurately detect any abnormality prior to failure and classify the health conditions of the target system. Recently, fault diagnosis studies using deep learning have achieved excellent performance thanks to the ability of new methods to autonomously extract meaningful features. For this purpose, two types of signals of different resolutions are measured from rotating machinery, specifically: operation signals and vibration signals. Operation signals, which are measured with a low sampling rate, are obtained in real-time and contain various types of condition parameters that enable global monitoring of the system. Vibration signals with a high sampling rate are obtained when an event occurs, not in real-time. Using these signals of different resolutions, two sub-tasks of fault diagnosis – anomaly detection and fault identification – are performed. Anomaly detection, which is conducted with operation signals, is a task to detect abnormalities in a system before those abnormalities develop into a hard failure. This is considered macro-level fault diagnosis. When performing anomaly detection, the normal data is modeled by unsupervised learning, a residual is calculated, and a threshold is determined. If the residual becomes larger than the threshold, the system is regarded as an anomaly condition. Fault identification is performed to classify the health conditions of the system using vibration signals; this is viewed as micro-level fault diagnosis. For fault identification, supervised learning is used to train a deep-learning-based classifier; thus, a large amount of labeled data is required for the training. Since fault data is insufficient in real industrial fields, data augmentation is necessary to augment the fault data. Currently, a variational auto-encoder or a generative adversarial network are the approaches most widely used for data augmentation. Anomaly detection and fault identification have been studied separately. If both tasks are integrated, macro- and micro-level fault diagnosis can be implemented. However, there are three issues that must be handled to develop a deep-learning-based methodology for macro- and micro-level fault diagnosis. First, conventional anomaly detection methods produce frequent false alarms; in other words, they may indicate a problem even if there is no anomaly in the system. This problem occurs because conventional approaches may model the normal data inadequately or set a wrong threshold; for example, one that does not consider the fluctuations in the normal data. Second, the prior generative-network-based augmentation approach has inborn limitations due to its structural properties. With this method, signals of various lengths cannot be generated because the architecture is fixed. Also, incorrect samples can be generated if the latent vectors are sampled wrongly. The final issue with health classification is that the performance of a classifier can be affected by noise in the input data. Since noise can distort the data distribution, it is difficult for a classifier to correctly classify the noisy data. Based on the current state of the field, this doctoral dissertation proposes a deep-learning-based methodology for macro- and micro-level fault diagnosis using operation and vibration signals from rotating machinery. The first research thrust proposes new methods for modeling and threshold setting to reduce false alarms related to anomaly detection. The proposed modeling method is developed by applying ensemble and denoising techniques to auto-encoders. Further, a threshold is newly proposed using the joint distribution of the output and the residual. Consequently, the proposed method considers the fluctuations in the normal data, which can significantly reduce false alarms. The second research thrust proposes a new generative network to generate signals of variable lengths. The proposed network, whose input and output are the time and amplitude, respectively, is designed to learn the frequency information of the training data. The proposed method is implemented to reflect the signal processing knowledge, including the use of the Nyquist theorem. After the training is finished, the proposed model can produce signals of various lengths in the desired time range. The proposed approach can also focus on the characteristic frequency components, thanks to attention blocks. The third research thrust proposes a novel training method that simultaneously learns the classification and denoising tasks. In the proposed scheme, multi-task learning is used to allow a classifier to solve the classification and denoising tasks concurrently. The proposed method can be applied to any deep-learning algorithm, regardless of the network type. The classifier that is trained by the proposed method can classify the health conditions, as well as remove noise in the input signals.회전기계는 제조 및 발전과 같이 다양한 산업 현장에서 널리 사용되고 있다. 회전기계의 예기치 못한 고장은 막대한 경제적, 인적 손실을 야기할 수 있다. 이러한 상황을 예방하기 위해서, 회전기계의 건전성 상태를 정확히 관리하는 것을 목표로 하는 고장 진단 연구가 주목을 받고 있다. 고장 진단 기법들은 목표 시스템의 이상을 정확히 감지하고 건전성 상태를 식별하는 것을 목표로 한다. 최근에는 딥러닝 기반 연구들이 자동적으로 유의미한 특성인자를 추출하는 능력 덕분에 뛰어난 진단 성능을 보이고 있다. 회전기계에서는 해상도가 서로 다른 운전 신호 및 진동 신호가 취득된다. 저샘플링 주파수로 취득되는 운전 신호는 실시간으로 얻어지고, 시스템을 전반적으로 관리할 수 있는 다양한 종류의 상태 변수를 포함하고 있다. 진동 신호는 고샘플링 주파수로 측정되고 실시간이 아니라, 고장이 발생하면 취득된다. 해상도가 다른 두 신호를 활용해서 고장 진단의 두 가지 하위 테스크인 이상 감지 및 고장 식별이 수행된다. 운전 신호를 가지고 수행되는 이상 감지는 시스템의 이상을 가능하면 빨리 감지하는 것을 목표로 한다. 이것은 거시적 수준의 고장 진단으로 여겨진다. 이상 감지 수행 시, 정상 데이터는 비지도 학습 방식으로 모델링 되고, 잔차 신호가 계산된 후에 기준치가 결정된다. 잔차 신호가 기준치를 초과하면, 해당 시스템은 이상이 있다고 판단된다. 고장 식별은 진동 신호를 사용해서 시스템의 건전성 상태를 분류하는 것을 목표로 한다. 이것은 미시적 수준의 고장 진단으로 여겨진다. 지도학습 방식을 활용해 딥러닝 기반 진단기를 학습시킨다. 그러므로 많은 양의 라벨 데이터가 학습에 필요하다. 실제 산업 현장에서는 고장 데이터가 부족하기 때문에, 부족한 고장 데이터를 증량하기 위한 데이터 증량 기법이 필수적이다. 최근에는 변분적 오토인코더나 적대적 생성 신경망을 활용한 증량 기법이 널리 연구되고 있다. 이상 감지와 고장 식별은 각자 따로 연구되었다. 만약 두 테스크가 통합된다면, 거시적 및 미시적 고장 진단이 수행될 수 있다. 하지만, 딥러닝 기반 거시적 및 미시적 고장 진단 기법을 개발하는 데 해결해야 할 세 가지 문제점이 있다. 첫째, 기존 이상 감지 기법들은 시스템에 아무 이상이 없어도 오감지를 빈번하게 발생시켰다. 기존 방법들은 정상 데이터를 부정확하게 모델링하거나 기준치를 잘못 설정해서 정상 데이터에 존재하는 변동을 고려하지 못한다. 둘째, 기존 생성 신경망 기반 모델들은 구조적 특징에 기인한 한계점을 갖고 있다. 다양한 길이의 신호가 만들어질 수 없고, 잠재 벡터가 잘못 샘플링되면 잘못된 샘플이 생성될 수 있다. 건전성 분류와 관련된 마지막 이슈는 분류기의 성능이 입력 데이터의 노이즈에 영향을 받을 수 있다는 점이다. 노이즈는 데이터 분포를 왜곡할 수 있기 때문에, 분류기가 노이즈가 있는 데이터를 올바르게 분류하는 것은 어렵다. 이러한 현황을 바탕으로, 본 박사학위 논문에서는 회전기계 내 운전 및 진동 신호를 활용한 딥러닝 기반 거시적 및 미시적 고장 진단 기법을 제안한다. 첫 번째 연구는 오감지를 줄이는 이상 감지를 위해서, 새로운 모델링 및 기준치 설정 기법들을 제안한다. 제안하는 모델링 방법은 오토인코더에 앙상블 및 디노이징 기법을 적용하여 개발됐다. 또한, 결과값과 잔차 신호 사이의 결합분포를 사용해서 동적 기준치를 설정하는 기법도 개발됐다. 이를 통해, 제안하는 방법은 정상 데이터의 변동을 고려하여 오감지를 상당히 줄일 수 있다. 두 번째 연구에서는 다양한 길이의 신호를 만들기 위한 새로운 생성 모델을 제안한다. 제안하는 네트워크는 입력과 출력이 시간 및 진폭이고, 학습 데이터의 주파수 정보를 학습하도록 설계됐다. 제안하는 모델은 나이키스트 이론과 같은 신호 처리 지식을 반영하기 위해서 신중히 설계됐다. 학습 후에, 제안하는 방법은 원하는 시간대의 다양한 길이의 신호를 만들 수 있다. 또한, 제안하는 네트워크는 어텐션 블록 덕분에 특성 주파수 성분에 집중할 수 있다. 세 번째 연구는 분류와 디노이징 테스크를 동시에 배우는 학습 기법을 제안한다. 제안하는 기법에서는 두 가지 테스크를 동시에 학습하기 위해서 다중 테스크 학습 기법이 사용된다. 제안하는 기법은 네트워크 종류에 상관없이 어떠한 딥러닝 알고리즘에 적용될 수 있다. 제안하는 방법으로 학습된 분류기는 건전성 상태를 잘 분류할 뿐만 아니라, 입력 신호의 노이즈도 제거할 수 있다.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 5 1.3 Dissertation Layout 9 Chapter 2 Technical Background and Literature Review 10 2.1 Fault Diagnosis Methods of Rotating Machinery 10 2.2 Low- and High-resolution Signals from Rotating Machinery 13 2.3 Review of Deep Learning Algorithms 15 2.3.1 One-dimensional Convolutional Neural Network (1D CNN) 16 2.3.2 Long Short-term Memory (LSTM) 17 2.4 Deep-learning-based Macro- and Micro-level Fault Diagnosis Methods 19 2.4.1 Anomaly Detection 23 2.4.2 Data Augmentation 28 2.4.3 Health Classification 32 2.5 Summary and Discussion 35 Chapter 3 Ensemble Denoising Auto-encoder-based Dynamic Threshold (EDAE-DT) for Anomaly Detection 37 3.1 Background: Deep-learning-based Anomaly Detection 39 3.1.1 Conventional Methods to Model the Normal Data 39 3.1.2 Conventional Methods to Set a Threshold 41 3.2 Ensemble Denoising Auto-encoder-based Dynamic Threshold (EDAE-DT) 42 3.3 Performance Evaluation Metrics 47 3.4 Description of the Validation Datasets 50 3.5 Validation of the Proposed Method 58 3.5.1 Case Study 1: Dataset A1 58 3.5.2 Case Study 2: Dataset A2 74 3.5.3 Analysis and Discussion 89 3.6 Summary and Discussion 95 Chapter 4 Frequency-learning Generative Network (FLGN) for Data Augmentation 96 4.1 Background: Fourier Series 97 4.2 Frequency-learning Generative Network (FLGN) 99 4.2.1 Problem Formulation 99 4.2.2 Overall Procedure of FLGN 100 4.2.3 Deep-learning Implementation Details to Reflect Signals Processing Knowledge 105 4.3 Experimental Implementation Setting 106 4.3.1 Hyper-parameter Setting 107 4.3.2 Evaluation Scheme 107 4.4 Description of the Validation Datasets 111 4.5 Validation of the Proposed Method 119 4.5.1 Case Study 1: Simulated Signal 119 4.5.2 Case Study 2: RK4 Testbed Dataset 128 4.5.3 Case Study 3: MAFAULDA 141 4.5.4 Analysis and Discussion 153 4.6 Summary and Discussion 158 Chapter 5 Multi-task Learning of Classification and Denoising (MLCD) for Health Classification 159 5.1 Background: Multi-task Learning 160 5.2 Multi-task Learning of Classification and Denoising (MLCD) 161 5.2.1 Overall Procedure of MLCD 162 5.2.2 Integration with LSTM: MLCD-LSTM 165 5.2.3 Integration with 1D CNN: MLCD-1D CNN 166 5.3 Preprocessing Techniques 170 5.4 Description of the Validation Datasets 172 5.5 Validation of the Proposed Method 176 5.5.1 Case Study 1: MLCD-LSTM 176 5.5.2 Case Study 2: MLCD-1D CNN 183 5.6 Summary and Discussion 190 Chapter 6 Conclusion 191 6.1 Contributions and Significance 191 6.2 Suggestions for Future Research 194 References 196 국문 초록 209박