Support matrix machine: A review

Support vector machine (SVM) is one of the most studied paradigms in the realm of machine learning for classification and regression problems. It relies on vectorized input data. However, a significant portion of the real-world data exists in matrix format, which is given as input to SVM by reshaping the matrices into vectors. The process of reshaping disrupts the spatial correlations inherent in the matrix data. Also, converting matrices into vectors results in input data with a high dimensionality, which introduces significant computational complexity. To overcome these issues in classifying matrix input data, support matrix machine (SMM) is proposed. It represents one of the emerging methodologies tailored for handling matrix input data. The SMM method preserves the structural information of the matrix data by using the spectral elastic net property which is a combination of the nuclear norm and Frobenius norm. This article provides the first in-depth analysis of the development of the SMM model, which can be used as a thorough summary by both novices and experts. We discuss numerous SMM variants, such as robust, sparse, class imbalance, and multi-class classification models. We also analyze the applications of the SMM model and conclude the article by outlining potential future research avenues and possibilities that may motivate academics to advance the SMM algorithm

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process

Robust-MBFD: A Robust Deep Learning System for Motor Bearing Faults Detection Using Multiple Deep Learning Training Strategies and A Novel Double Loss Function

This paper presents a comprehensive analysis of motor bearing fault detection (MBFD), which involves the task of identifying faults in a motor bearing based on its vibration. To this end, we first propose and evaluate various machine learning based systems for the MBFD task. Furthermore, we propose three deep learning based systems for the MBFD task, each of which explores one of the following training strategies: supervised learning, semi-supervised learning, and unsupervised learning. The proposed machine learning based systems and deep learning based systems are evaluated, compared, and then they are used to identify the best model for the MBFD task. We conducted extensive experiments on various benchmark datasets of motor bearing faults, including those from the American Society for Mechanical Failure Prevention Technology (MFPT), Case Western Reserve University Bearing Center (CWRU), and the Condition Monitoring of Bearing Damage in Electromechanical Drive Systems from Paderborn University (PU). The experimental results on different datasets highlight two main contributions of this study. First, we prove that deep learning based systems are more effective than machine learning based systems for the MBFD task. Second, we achieve a robust and general deep learning based system with a novel loss function for the MBFD task on several benchmark datasets, demonstrating its potential for real-life MBFD applications

Lifetime Based Health Indicator for Bearings using Convolitional Neural Networks

Master's thesis Renewable Energy ENE500 - University of Agder 2019Out of all the components in rotating electrical machinery, bearings have the highest failure rate. Bearingdegradation is a seemingly random process which is hard to both model and predict. Countless of con-dition based methods and algorithms have been proposed in order to accurately diagnose incipient faultsand estimate the remaining useful lifetime of bearings. These methods are often complex and hard to im-plement. In this thesis, a data-driven method of estimating a linear lifetime based health indicator (HI)using convolutional neural networks (CNNs) is proposed. The idea behind the method is to train a CNNmodel to recognize the shapes and distributions of vibration data in order to predict a HI with minimalpre-processing. Two models are presented: A CNN that takes time-series vibration data as input and aCNN that takes vibration frequency spectrum data as input. Finally, HIs are predicted on unique datasetsand their respective remaining useful lifetimes (RULs) are estimated as part of the model validation process.The results show that the models are able to recognize relevant fault features to a certain degree. However, accurate predictions have proven difficult in many cases

딥러닝 기반 고장 진단을 위한 정보 활용 극대화 기법 개발

학위논문(박사) -- 서울대학교대학원 : 공과대학 기계항공공학부, 2021.8. 윤병동.기계 시스템의 예기치 않은 고장은 많은 산업 분야에서 막대한 사회적, 경제적 손실을 야기할 수 있다. 갑작스런 고장을 감지하고 예방하여 기계 시스템의 신뢰성을 높이기 위해 데이터 기반 고장 진단 기술을 개발하기 위한 연구가 활발하게 이루어지고 있다. 고장 진단 기술의 목표는 대상 기계 시스템의 고장 발생을 가능한 빨리 감지하고 진단하는 것이다. 최근 합성곱 신경망 기법을 포함한 딥러닝 기반 고장 진단 기술은 자율적인 특성인자(feature) 학습이 가능하고 높은 진단 성능을 얻을 수 있다는 장점이 있어 활발히 연구되고 있다. 그러나 딥러닝 기반의 고장 진단 기술을 개발함에 있어 해결해야 할 몇 가지 문제점들이 존재한다. 먼저, 신경망 구조를 깊게 쌓음으로써 풍부한 계층적 특성인자들을 배울 수 있고, 이를 통해 향상된 성능을 얻을 수 있다. 그러나 기울기(gradient) 정보 흐름의 비효율성과 과적합 문제로 인해 모델이 깊어질수록 학습이 어렵게 된다는 문제가 있다. 다음으로, 높은 성능의 고장 진단 모델을 학습하기 위해서는 충분한 양의 레이블 데이터(labeled data)가 확보돼야 한다. 그러나 실제 현장에서 운용되고 있는 기계 시스템의 경우, 충분한 양의 데이터와 레이블 정보를 얻는 것이 어려운 경우가 많다. 따라서 이러한 문제들을 해결하고 진단 성능을 향상시키기 위한 새로운 딥러닝 기반 고장 진단 기술의 개발이 필요하다. 본 박사학위논문에서는 딥러닝 기반 고장 진단 기술의 성능을 향상시키기 위한 세가지 정보 활용 극대화 기법에 대한 연구로 1) 딥러닝 아키텍처 내 기울기 정보 흐름을 향상시키기 위한 새로운 딥러닝 구조 연구, 2) 파라미터 전이 및 삼중항 손실을 기반으로 불충분한 데이터 및 노이즈 조건 하 강건하고 차별적인 특성인자 학습에 대한 연구, 3) 다른 도메인으로부터 레이블 정보를 전이시켜 사용하는 도메인 적응 기반 고장 진단 기법 연구를 제안한다. 첫 번째 연구에서는 딥러닝 모델 내 기울기 정보 흐름을 개선하기 위한 향상된 합성곱 신경망 기반 구조를 제안한다. 본 연구에서는 다양한 계층의 아웃풋(feature map)을 직접 연결함으로써 향상된 정보 흐름을 얻을 수 있으며, 그 결과 진단 모델을 효율적으로 학습하는 것이 가능하다. 또한 차원 축소 모듈을 통해 학습 파라미터 수를 크게 줄임으로써 학습 효율성을 높일 수 있다. 두 번째 연구에서는 파라미터 전이 및 메트릭 학습 기반 고장 진단 기법을 제안한다. 본 연구는 데이터가 불충분하고 노이즈가 많은 조건 하에서도 높은 고장 진단 성능을 얻기 위해 강건하고 차별적인 특성인자 학습을 가능하게 한다. 먼저, 풍부한 소스 도메인 데이터를 사용해 훈련된 사전학습모델을 타겟 도메인으로 전이해 사용함으로써 강건한 진단 방법을 개발할 수 있다. 또한, semi-hard 삼중항 손실 함수를 사용함으로써 각 상태 레이블에 따라 데이터가 더 잘 분리되도록 해주는 특성인자를 학습할 수 있다. 세 번째 연구에서는 레이블이 지정되지 않은(unlabeled) 대상 도메인에서의 고장 진단 성능을 높이기 위한 레이블 정보 전이 전략을 제안한다. 우리가 목표로 하는 대상 도메인에서의 고장 진단 방법을 개발하기 위해 다른 소스 도메인에서 얻은 레이블 정보가 전이되어 활용된다. 동시에 새롭게 고안한 의미론적 클러스터링 손실(semantic clustering loss)을 여러 특성인자 수준에 적용함으로써 차별적인 도메인 불변 기능을 학습한다. 결과적으로 도메인 불변 특성을 가지며 의미론적으로 잘 분류되는 특성인자를 효과적으로 학습할 수 있음을 증명하였다.Unexpected failures of mechanical systems can lead to substantial social and financial losses in many industries. In order to detect and prevent sudden failures and to enhance the reliability of mechanical systems, significant research efforts have been made to develop data-driven fault diagnosis techniques. The purpose of fault diagnosis techniques is to detect and identify the occurrence of abnormal behaviors in the target mechanical systems as early as possible. Recently, deep learning (DL) based fault diagnosis approaches, including the convolutional neural network (CNN) method, have shown remarkable fault diagnosis performance, thanks to their autonomous feature learning ability. Still, there are several issues that remain to be solved in the development of robust and industry-applicable deep learning-based fault diagnosis techniques. First, by stacking the neural network architectures deeper, enriched hierarchical features can be learned, and therefore, improved performance can be achieved. However, due to inefficiency in the gradient information flow and overfitting problems, deeper models cannot be trained comprehensively. Next, to develop a fault diagnosis model with high performance, it is necessary to obtain sufficient labeled data. However, for mechanical systems that operate in real-world environments, it is not easy to obtain sufficient data and label information. Consequently, novel methods that address these issues should be developed to improve the performance of deep learning based fault diagnosis techniques. This dissertation research investigated three research thrusts aimed toward maximizing the use of information to improve the performance of deep learning based fault diagnosis techniques, specifically: 1) study of the deep learning structure to enhance the gradient information flow within the architecture, 2) study of a robust and discriminative feature learning method under insufficient and noisy data conditions based on parameter transfer and triplet loss, and 3) investigation of a domain adaptation based fault diagnosis method that propagates the label information across different domains. The first research thrust suggests an advanced CNN-based architecture to improve the gradient information flow within the deep learning model. By directly connecting the feature maps of different layers, the diagnosis model can be trained efficiently thanks to enhanced information flow. In addition, the dimension reduction module also can increase the training efficiency by significantly reducing the number of trainable parameters. The second research thrust suggests a parameter transfer and metric learning based fault diagnosis method. The proposed approach facilitates robust and discriminative feature learning to enhance fault diagnosis performance under insufficient and noisy data conditions. The pre-trained model trained using abundant source domain data is transferred and used to develop a robust fault diagnosis method. Moreover, a semi-hard triplet loss function is adopted to learn the features with high separability, according to the class labels. Finally, the last research thrust proposes a label information propagation strategy to increase the fault diagnosis performance in the unlabeled target domain. The label information obtained from the source domain is transferred and utilized for developing fault diagnosis methods in the target domain. Simultaneously, the newly devised semantic clustering loss is applied at multiple feature levels to learn discriminative, domain-invariant features. As a result, features that are not only semantically well-clustered but also domain-invariant can be effectively learned.Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Research Scope and Overview 3 1.3 Dissertation Layout 6 Chapter 2 Technical Background and Literature Review 8 2.1 Fault Diagnosis Techniques for Mechanical Systems 8 2.1.1 Fault Diagnosis Techniques 10 2.1.2 Deep Learning Based Fault Diagnosis Techniques 15 2.2 Transfer Learning 22 2.3 Metric Learning 28 2.4 Summary and Discussion 30 Chapter 3 Direct Connection Based Convolutional Neural Network (DC-CNN) for Fault Diagnosis 31 3.1 Directly Connected Convolutional Module 33 3.2 Dimension Reduction Module 34 3.3 Input Vibration Image Generation 36 3.4 DC-CNN-Based Fault Diagnosis Method 40 3.5 Experimental Studies and Results 45 3.5.1 Experiment and Data Description 45 3.5.2 Compared Methods 48 3.5.3 Diagnosis Performance Results 51 3.5.4 The Number of Trainable Parameters 56 3.5.5 Visualization of the Learned Features 58 3.5.6 Robustness of Diagnosis Performance 62 3.6 Summary and Discussion 67 Chapter 4 Robust and Discriminative Feature Learning for Fault Diagnosis Under Insufficient and Noisy Data Conditions 68 4.1 Parameter transfer learning 70 4.2 Robust Feature Learning Based on the Pre-trained model 72 4.3 Discriminative Feature Learning Based on the Triplet loss 77 4.4 Robust and Discriminative Feature Learning for Fault Diagnosis 80 4.5 Experimental Studies and Results 84 4.5.1 Experiment and Data Description 84 4.5.2 Compared Methods 85 4.5.3 Experimental Results Under Insufficient Data Conditions 86 4.5.4 Experimental Results Under Noisy Data Conditions 92 4.6 Summary and Discussion 95 Chapter 5 A Domain Adaptation with Semantic Clustering (DASC) Method for Fault Diagnosis 96 5.1 Unsupervised Domain Adaptation 101 5.2 CNN-based Diagnosis Model 104 5.3 Learning of Domain-invariant Features 105 5.4 Domain Adaptation with Semantic Clustering 107 5.5 Proposed DASC-based Fault Diagnosis Method 109 5.6 Experimental Studies and Results 114 5.6.1 Experiment and Data Description 114 5.6.2 Compared Methods 117 5.6.3 Scenario I: Different Operating Conditions 118 5.6.4 Scenario II: Different Rotating Machinery 125 5.6.5 Analysis and Discussion 131 5.7 Summary and Discussion 140 Chapter 6 Conclusion 141 6.1 Contributions and Significance 141 6.2 Suggestions for Future Research 143 References 146 국문 초록 154박

Challenges and opportunities of deep learning models for machinery fault detection and diagnosis: a review

In the age of industry 4.0, deep learning has attracted increasing interest for various research applications. In recent years, deep learning models have been extensively implemented in machinery fault detection and diagnosis (FDD) systems. The deep architecture's automated feature learning process offers great potential to solve problems with traditional fault detection and diagnosis (TFDD) systems. TFDD relies on manual feature selection, which requires prior knowledge of the data and is time intensive. However, the high performance of deep learning comes with challenges and costs. This paper presents a review of deep learning challenges related to machinery fault detection and diagnosis systems. The potential for future work on deep learning implementation in FDD systems is briefly discussed

Condition Monitoring Methods for Large, Low-speed Bearings

In all industrial production plants, well-functioning machines and systems are required for sustained and safe operation. However, asset performance degrades over time and may lead to reduced effiency, poor product quality, secondary damage to other assets or even complete failure and unplanned downtime of critical systems. Besides the potential safety hazards from machine failure, the economic consequences are large, particularly in offshore applications where repairs are difficult. This thesis focuses on large, low-speed rolling element bearings, concretized by the main swivel bearing of an offshore drilling machine. Surveys have shown that bearing failure in drilling machines is a major cause of rig downtime. Bearings have a finite lifetime, which can be estimated using formulas supplied by the bearing manufacturer. Premature failure may still occur as a result of irregularities in operating conditions and use, lubrication, mounting, contamination, or external environmental factors. On the contrary, a bearing may also exceed the expected lifetime. Compared to smaller bearings, historical failure data from large, low-speed machinery is rare. Due to the high cost of maintenance and repairs, the preferred maintenance arrangement is often condition based. Vibration measurements with accelerometers is the most common data acquisition technique. However, vibration based condition monitoring of large, low-speed bearings is challenging, due to non-stationary operating conditions, low kinetic energy and increased distance from fault to transducer. On the sensor side, this project has also investigated the usage of acoustic emission sensors for condition monitoring purposes. Roller end damage is identified as a failure mode of interest in tapered axial bearings. Early stage abrasive wear has been observed on bearings in drilling machines. The failure mode is currently only detectable upon visual inspection and potentially through wear debris in the bearing lubricant. In this thesis, multiple machine learning algorithms are developed and applied to handle the challenges of fault detection in large, low-speed bearings with little or no historical data and unknown fault signatures. The feasibility of transfer learning is demonstrated, as an approach to speed up implementation of automated fault detection systems when historical failure data is available. Variational autoencoders are proposed as a method for unsupervised dimensionality reduction and feature extraction, being useful for obtaining a health indicator with a statistical anomaly detection threshold. Data is collected from numerous experiments throughout the project. Most notably, a test was performed on a real offshore drilling machine with roller end wear in the bearing. To replicate this failure mode and aid development of condition monitoring methods, an axial bearing test rig has been designed and built as a part of the project. An overview of all experiments, methods and results are given in the thesis, with details covered in the appended papers.publishedVersio

State of AI-based monitoring in smart manufacturing and introduction to focused section

Over the past few decades, intelligentization, supported by artificial intelligence (AI) technologies, has become an important trend for industrial manufacturing, accelerating the development of smart manufacturing. In modern industries, standard AI has been endowed with additional attributes, yielding the so-called industrial artificial intelligence (IAI) that has become the technical core of smart manufacturing. AI-powered manufacturing brings remarkable improvements in many aspects of closed-loop production chains from manufacturing processes to end product logistics. In particular, IAI incorporating domain knowledge has benefited the area of production monitoring considerably. Advanced AI methods such as deep neural networks, adversarial training, and transfer learning have been widely used to support both diagnostics and predictive maintenance of the entire production process. It is generally believed that IAI is the critical technologies needed to drive the future evolution of industrial manufacturing. This article offers a comprehensive overview of AI-powered manufacturing and its applications in monitoring. More specifically, it summarizes the key technologies of IAI and discusses their typical application scenarios with respect to three major aspects of production monitoring: fault diagnosis, remaining useful life prediction, and quality inspection. In addition, the existing problems and future research directions of IAI are also discussed. This article further introduces the papers in this focused section on AI-based monitoring in smart manufacturing by weaving them into the overview, highlighting how they contribute to and extend the body of literature in this area

Ensemble deep learning: A review

Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions