Signal processing and graph-based semi-supervised learning-based fault diagnosis for direct online induction motors

In this thesis, fault diagnosis approaches for direct online induction motors are proposed using signal processing and graph-based semi-supervised learning (GSSL). These approaches are developed using experimental data obtained in the lab for two identical 0.25 HP three-phase squirrel-cage induction motors. Various electrical and mechanical single- and multi-faults are applied to each motor during experiments. Three-phase stator currents and three-dimensional vibration signals are recorded simultaneously in each experiment. In this thesis, Power Spectral Density (PSD)-based stator current amplitude spectrum analysis and one-dimensional Complex Continuous Wavelet Transform (CWT)-based stator current time-scale spectrum analysis are employed to detect broken rotor bar (BRB) faults. An effective single- and multi-fault diagnosis approach is developed using GSSL, where discrete wavelet transform (DWT) is applied to extract features from experimental stator current and vibration data. Three GSSL algorithms (Local and global consistency (LGC), Gaussian field and harmonic functions (GFHF), and greedy-gradient max-cut (GGMC)) are adopted and compared in this study. To enable machine learning for untested motor operating conditions, mathematical equations to calculate features for untested conditions are developed using curve fitting and features obtained from experimental data of tested conditions

Artificial Intelligence in Process Engineering

In recent years, the field of Artificial Intelligence (AI) is experiencing a boom, caused by recent breakthroughs in computing power, AI techniques, and software architectures. Among the many fields being impacted by this paradigm shift, process engineering has experienced the benefits caused by AI. However, the published methods and applications in process engineering are diverse, and there is still much unexploited potential. Herein, the goal of providing a systematic overview of the current state of AI and its applications in process engineering is discussed. Current applications are described and classified according to a broader systematic. Current techniques, types of AI as well as pre- and postprocessing will be examined similarly and assigned to the previously discussed applications. Given the importance of mechanistic models in process engineering as opposed to the pure black box nature of most of AI, reverse engineering strategies as well as hybrid modeling will be highlighted. Furthermore, a holistic strategy will be formulated for the application of the current state of AI in process engineering

매개분포근사를 통한 공정시스템 공학에서의 확률기계학습 접근법

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부, 2021.8. 이종민.With the rapid development of measurement technology, higher quality and vast amounts of process data become available. Nevertheless, process data are ‘scarce’ in many cases as they are sampled only at certain operating conditions while the dimensionality of the system is large. Furthermore, the process data are inherently stochastic due to the internal characteristics of the system or the measurement noises. For this reason, uncertainty is inevitable in process systems, and estimating it becomes a crucial part of engineering tasks as the prediction errors can lead to misguided decisions and cause severe casualties or economic losses. A popular approach to this is applying probabilistic inference techniques that can model the uncertainty in terms of probability. However, most of the existing probabilistic inference techniques are based on recursive sampling, which makes it difficult to use them for industrial applications that require processing a high-dimensional and massive amount of data. To address such an issue, this thesis proposes probabilistic machine learning approaches based on parametric distribution approximation, which can model the uncertainty of the system and circumvent the computational complexity as well. The proposed approach is applied for three major process engineering tasks: process monitoring, system modeling, and process design. First, a process monitoring framework is proposed that utilizes a probabilistic classifier for fault classification. To enhance the accuracy of the classifier and reduce the computational cost for its training, a feature extraction method called probabilistic manifold learning is developed and applied to the process data ahead of the fault classification. We demonstrate that this manifold approximation process not only reduces the dimensionality of the data but also casts the data into a clustered structure, making the classifier have a low dependency on the type and dimension of the data. By exploiting this property, non-metric information (e.g., fault labels) of the data is effectively incorporated and the diagnosis performance is drastically improved. Second, a probabilistic modeling approach based on Bayesian neural networks is proposed. The parameters of deep neural networks are transformed into Gaussian distributions and trained using variational inference. The redundancy of the parameter is autonomously inferred during the model training, and insignificant parameters are eliminated a posteriori. Through a verification study, we demonstrate that the proposed approach can not only produce high-fidelity models that describe the stochastic behaviors of the system but also produce the optimal model structure. Finally, a novel process design framework is proposed based on reinforcement learning. Unlike the conventional optimization methods that recursively evaluate the objective function to find an optimal value, the proposed method approximates the objective function surface by parametric probabilistic distributions. This allows learning the continuous action policy without introducing any cumbersome discretization process. Moreover, the probabilistic policy gives means for effective control of the exploration and exploitation rates according to the certainty information. We demonstrate that the proposed framework can learn process design heuristics during the solution process and use them to solve similar design problems.계측기술의 발달로 양질의, 그리고 방대한 양의 공정 데이터의 취득이 가능해졌다. 그러나 많은 경우 시스템 차원의 크기에 비해서 일부 운전조건의 공정 데이터만이 취득되기 때문에, 공정 데이터는 ‘희소’하게 된다. 뿐만 아니라, 공정 데이터는 시스템 거동 자체와 더불어 계측에서 발생하는 노이즈로 인한 본질적인 확률적 거동을 보인다. 따라서 시스템의 예측모델은 예측 값에 대한 불확실성을 정량적으로 기술하는 것이 요구되며, 이를 통해 오진을 예방하고 잠재적 인명 피해와 경제적 손실을 방지할 수 있다. 이에 대한 보편적인 접근법은 확률추정기법을 사용하여 이러한 불확실성을 정량화 하는 것이나, 현존하는 추정기법들은 재귀적 샘플링에 의존하는 특성상 고차원이면서도 다량인 공정데이터에 적용하기 어렵다는 근본적인 한계를 가진다. 본 학위논문에서는 매개분포근사에 기반한 확률기계학습을 적용하여 시스템에 내재된 불확실성을 모델링하면서도 동시에 계산 효율적인 접근 방법을 제안하였다. 먼저, 공정의 모니터링에 있어 가우시안 혼합 모델 (Gaussian mixture model)을 분류자로 사용하는 확률적 결함 분류 프레임워크가 제안되었다. 이때 분류자의 학습에서의 계산 복잡도를 줄이기 위하여 데이터를 저차원으로 투영시키는데, 이를 위한 확률적 다양체 학습 (probabilistic manifold learn-ing) 방법이 제안되었다. 제안하는 방법은 데이터의 다양체 (manifold)를 근사하여 데이터 포인트 사이의 쌍별 우도 (pairwise likelihood)를 보존하는 투영법이 사용된다. 이를 통하여 데이터의 종류와 차원에 의존도가 낮은 진단 결과를 얻음과 동시에 데이터 레이블과 같은 비거리적 (non-metric) 정보를 효율적으로 사용하여 결함 진단 능력을 향상시킬 수 있음을 보였다. 둘째로, 베이지안 심층 신경망(Bayesian deep neural networks)을 사용한 공정의 확률적 모델링 방법론이 제시되었다. 신경망의 각 매개변수는 가우스 분포로 치환되며, 변분추론 (variational inference)을 통하여 계산 효율적인 훈련이 진행된다. 훈련이 끝난 후 파라미터의 유효성을 측정하여 불필요한 매개변수를 소거하는 사후 모델 압축 방법이 사용되었다. 반도체 공정에 대한 사례 연구는 제안하는 방법이 공정의 복잡한 거동을 효과적으로 모델링 할 뿐만 아니라 모델의 최적 구조를 도출할 수 있음을 보여준다. 마지막으로, 분포형 심층 신경망을 사용한 강화학습을 기반으로 한 확률적 공정 설계 프레임워크가 제안되었다. 최적치를 찾기 위해 재귀적으로 목적 함수 값을 평가하는 기존의 최적화 방법론과 달리, 목적 함수 곡면 (objective function surface)을 매개화 된 확률분포로 근사하는 접근법이 제시되었다. 이를 기반으로 이산화 (discretization)를 사용하지 않고 연속적 행동 정책을 학습하며, 확실성 (certainty)에 기반한 탐색 (exploration) 및 활용 (exploi-tation) 비율의 제어가 효율적으로 이루어진다. 사례 연구 결과는 공정의 설계에 대한 경험지식 (heuristic)을 학습하고 유사한 설계 문제의 해를 구하는 데 이용할 수 있음을 보여준다.Chapter 1 Introduction 1 1.1. Motivation 1 1.2. Outline of the thesis 5 Chapter 2 Backgrounds and preliminaries 9 2.1. Bayesian inference 9 2.2. Monte Carlo 10 2.3. Kullback-Leibler divergence 11 2.4. Variational inference 12 2.5. Riemannian manifold 13 2.6. Finite extended-pseudo-metric space 16 2.7. Reinforcement learning 16 2.8. Directed graph 19 Chapter 3 Process monitoring and fault classification with probabilistic manifold learning 20 3.1. Introduction 20 3.2. Methods 25 3.2.1. Uniform manifold approximation 27 3.2.2. Clusterization 28 3.2.3. Projection 31 3.2.4. Mapping of unknown data query 32 3.2.5. Inference 33 3.3. Verification study 38 3.3.1. Dataset description 38 3.3.2. Experimental setup 40 3.3.3. Process monitoring 43 3.3.4. Projection characteristics 47 3.3.5. Fault diagnosis 50 3.3.6. Computational Aspects 56 Chapter 4 Process system modeling with Bayesian neural networks 59 4.1. Introduction 59 4.2. Methods 63 4.2.1. Long Short-Term Memory (LSTM) 63 4.2.2. Bayesian LSTM (BLSTM) 66 4.3. Verification study 68 4.3.1. System description 68 4.3.2. Estimation of the plasma variables 71 4.3.3. Dataset description 72 4.3.4. Experimental setup 72 4.3.5. Weight regularization during training 78 4.3.6. Modeling complex behaviors of the system 80 4.3.7. Uncertainty quantification and model compression 85 Chapter 5 Process design based on reinforcement learning with distributional actor-critic networks 89 5.1. Introduction 89 5.2. Methods 93 5.2.1. Flowsheet hashing 93 5.2.2. Behavioral cloning 99 5.2.3. Neural Monte Carlo tree search (N-MCTS) 100 5.2.4. Distributional actor-critic networks (DACN) 105 5.2.5. Action masking 110 5.3. Verification study 110 5.3.1. System description 110 5.3.2. Experimental setup 111 5.3.3. Result and discussions 115 Chapter 6 Concluding remarks 120 6.1. Summary of the contributions 120 6.2. Future works 122 Appendix 125 A.1. Proof of Lemma 1 125 A.2. Performance indices for dimension reduction 127 A.3. Model equations for process units 130 Bibliography 132 초 록 149박

Recent advances in the application of deep learning for fault diagnosis of rotating machinery using vibration signals

Vibration measurement and monitoring are essential in a wide variety of applications. Vibration measurements are critical for diagnosing industrial machinery malfunctions because they provide information about the condition of the rotating equipment. Vibration analysis is considered the most effective method for predictive maintenance because it is used to troubleshoot instantaneous faults as well as periodic maintenance. Numerous studies conducted in this vein have been published in a variety of outlets. This review documents data-driven and recently published deep learning techniques for vibration-based condition monitoring. Numerous studies were obtained from two reputable indexing databases, Web of Science and Scopus. Following a thorough review, 59 studies were selected for synthesis. The selected studies are then systematically discussed to provide researchers with an in-depth view of deep learning-based fault diagnosis methods based on vibration signals. Additionally, a few remarks regarding future research directions are made, including graph-based neural networks, physics-informed ML, and a transformer convolutional network-based fault diagnosis method

On the requirements of digital twin-driven autonomous maintenance

Autonomy has become a focal point for research and development in many industries. Whilst this was traditionally achieved by modelling self-engineering behaviours at the component-level, efforts are now being focused on the sub-system and system-level through advancements in artificial intelligence. Exploiting its benefits requires some innovative thinking to integrate overarching concepts from big data analysis, digitisation, sensing, optimisation, information technology, and systems engineering. With recent developments in Industry 4.0, machine learning and digital twin, there has been a growing interest in adapting these concepts to achieve autonomous maintenance; the automation of predictive maintenance scheduling directly from operational data and for in-built repair at the systems-level. However, there is still ambiguity whether state-of-the-art developments are truly autonomous or they simply automate a process. In light of this, it is important to present the current perspectives about where the technology stands today and indicate possible routes for the future. As a result, this effort focuses on recent trends in autonomous maintenance before moving on to discuss digital twin as a vehicle for decision making from the viewpoint of requirements, whilst the role of AI in assisting with this process is also explored. A suggested framework for integrating digital twin strategies within maintenance models is also discussed. Finally, the article looks towards future directions on the likely evolution and implications for its development as a sustainable technolog

Industrial Applications: New Solutions for the New Era

This book reprints articles from the Special Issue "Industrial Applications: New Solutions for the New Age" published online in the open-access journal Machines (ISSN 2075-1702). This book consists of twelve published articles. This special edition belongs to the "Mechatronic and Intelligent Machines" section

Data mining for fault diagnosis in steel making process under industry 4.0

The concept of Industry 4.0 (I4.0) refers to the intelligent networking of machines and processes in the industry, which is enabled by cyber-physical systems (CPS) - a technology that utilises embedded networked systems to achieve intelligent control. CPS enable full traceability of production processes as well as comprehensive data assignments in real-time. Through real-time communication and coordination between "manufacturing things", production systems, in the form of Cyber-Physical Production Systems (CPPS), can make intelligent decisions. Meanwhile, with the advent of I4.0, it is possible to collect heterogeneous manufacturing data across various facets for fault diagnosis by using the industrial internet of things (IIoT) techniques. Under this data-rich environment, the ability to diagnose and predict production failures provides manufacturing companies with a strategic advantage by reducing the number of unplanned production outages. This advantage is particularly desired for steel-making industries. As a consecutive and compact manufacturing process, process downtime is a major concern for steel-making companies since most of the operations should be conducted within a certain temperature range. In addition, steel-making consists of complex processes that involve physical, chemical, and mechanical elements, emphasising the necessity for data-driven approaches to handle high-dimensionality problems. For a modern steel-making plant, various measurement devices are deployed throughout this manufacturing process with the advancement of I4.0 technologies, which facilitate data acquisition and storage. However, even though data-driven approaches are showing merits and being widely applied in the manufacturing context, how to build a deep learning model for fault prediction in the steel-making process considering multiple contributing facets and its temporal characteristic has not been investigated. Additionally, apart from the multitudinous data, it is also worthwhile to study how to represent and utilise the vast and scattered distributed domain knowledge along the steel-making process for fault modelling. Moreover, state-of-the-art does not iv Abstract address how such accumulated domain knowledge and its semantics can be harnessed to facilitate the fusion of multi-sourced data in steel manufacturing. In this case, the purpose of this thesis is to pave the way for fault diagnosis in steel-making processes using data mining under I4.0. This research is structured according to four themes. Firstly, different from the conventional data-driven research that only focuses on modelling based on numerical production data, a framework for data mining for fault diagnosis in steel-making based on multi-sourced data and knowledge is proposed. There are five layers designed in this framework, which are multi-sourced data and knowledge acquisition, data and knowledge processing, KG construction and graphical data transformation, KG-aided modelling for fault diagnosis and decision support for steel manufacturing. Secondly, another of the purposes of this thesis is to propose a predictive, data-driven approach to model severe faults in the steel-making process, where the faults are usually with multi-faceted causes. Specifically, strip breakage in cold rolling is selected as the modelling target since it is a typical production failure with serious consequences and multitudinous factors contributing to it. In actual steel-making practice, if such a failure can be modelled on a micro-level with an adequately predicted window, a planned stop action can be taken in advance instead of a passive fast stop which will often result in severe damage to equipment. In this case, a multifaceted modelling approach with a sliding window strategy is proposed. First, historical multivariate time-series data of a cold rolling process were extracted in a run-to-failure manner, and a sliding window strategy was adopted for data annotation. Second, breakage-centric features were identified from physics-based approaches, empirical knowledge and data-driven features. Finally, these features were used as inputs for strip breakage modelling using a Recurrent Neural Network (RNN). Experimental results have demonstrated the merits of the proposed approach. Thirdly, among the heterogeneous data surrounding multi-faceted concepts in steelmaking, a significant amount of data consists of rich semantic information, such as technical documents and production logs generated through the process. Also, there Abstract v exists vast domain knowledge regarding the production failures in steel-making, which has a long history. In this context, proper semantic technologies are desired for the utilisation of semantic data and domain knowledge in steel-making. In recent studies, a Knowledge Graph (KG) displays a powerful expressive ability and a high degree of modelling flexibility, making it a promising semantic network. However, building a reliable KG is usually time-consuming and labour-intensive, and it is common that KG needs to be refined or completed before using in industrial scenarios. In this case, a fault-centric KG construction approach is proposed based on a hierarchy structure refinement and relation completion. Firstly, ontology design based on hierarchy structure refinement is conducted to improve reliability. Then, the missing relations between each couple of entities were inferred based on existing knowledge in KG, with the aim of increasing the number of edges that complete and refine KG. Lastly, KG is constructed by importing data into the ontology. An illustrative case study on strip breakage is conducted for validation. Finally, multi-faceted modelling is often conducted based on multi-sourced data covering indispensable aspects, and information fusion is typically applied to cope with the high dimensionality and data heterogeneity. Besides the ability for knowledge management and sharing, KG can aggregate the relationships of features from multiple aspects by semantic associations, which can be exploited to facilitate the information fusion for multi-faceted modelling with the consideration of intra-facets relationships. In this case, process data is transformed into a stack of temporal graphs under the faultcentric KG backbone. Then, a Graph Convolutional Networks (GCN) model is applied to extract temporal and attribute correlation features from the graphs, with a Temporal Convolution Network (TCN) to conduct conceptual modelling using these features. Experimental results derived using the proposed approach, and GCN-TCN reveal the impacts of the proposed KG-aided fusion approach. This thesis aims to research data mining in steel-making processes based on multisourced data and scattered distributed domain knowledge, which provides a feasibility study for achieving Industry 4.0 in steel-making, specifically in support of improving quality and reducing costs due to production failures

Real-Time Fault Diagnosis of Permanent Magnet Synchronous Motor and Drive System

Permanent Magnet Synchronous Motors (PMSMs) have gained massive popularity in industrial applications such as electric vehicles, robotic systems, and offshore industries due to their merits of efficiency, power density, and controllability. PMSMs working in such applications are constantly exposed to electrical, thermal, and mechanical stresses, resulting in different faults such as electrical, mechanical, and magnetic faults. These faults may lead to efficiency reduction, excessive heat, and even catastrophic system breakdown if not diagnosed in time. Therefore, developing methods for real-time condition monitoring and detection of faults at early stages can substantially lower maintenance costs, downtime of the system, and productivity loss. In this dissertation, condition monitoring and detection of the three most common faults in PMSMs and drive systems, namely inter-turn short circuit, demagnetization, and sensor faults are studied. First, modeling and detection of inter-turn short circuit fault is investigated by proposing one FEM-based model, and one analytical model. In these two models, efforts are made to extract either fault indicators or adjustments for being used in combination with more complex detection methods. Subsequently, a systematic fault diagnosis of PMSM and drive system containing multiple faults based on structural analysis is presented. After implementing structural analysis and obtaining the redundant part of the PMSM and drive system, several sequential residuals are designed and implemented based on the fault terms that appear in each of the redundant sets to detect and isolate the studied faults which are applied at different time intervals. Finally, real-time detection of faults in PMSMs and drive systems by using a powerful statistical signal-processing detector such as generalized likelihood ratio test is investigated. By using generalized likelihood ratio test, a threshold was obtained based on choosing the probability of a false alarm and the probability of detection for each detector based on which decision was made to indicate the presence of the studied faults. To improve the detection and recovery delay time, a recursive cumulative GLRT with an adaptive threshold algorithm is implemented. As a result, a more processed fault indicator is achieved by this recursive algorithm that is compared to an arbitrary threshold, and a decision is made in real-time performance. The experimental results show that the statistical detector is able to efficiently detect all the unexpected faults in the presence of unknown noise and without experiencing any false alarm, proving the effectiveness of this diagnostic approach.publishedVersio

Sense and Respond

Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA; from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization); and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively). The next natural evolutionary step is to provide value by creating industrial cyber-physical assets with human-like intelligence. This will only be possible by further integrating strategic smart sensor technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards. This reprint of “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensor technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes