Novel deep cross-domain framework for fault diagnosis or rotary machinery in prognostics and health management

Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This doctoral research provides a systematic review of state-of-the-art deep learning-based PHM frameworks, an empirical analysis on bearing fault diagnosis benchmarks, and a novel multi-source domain adaptation framework. It emphasizes the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. Besides, the limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. The empirical study of the benchmarks highlights the evaluation results of the existing models on bearing fault diagnosis benchmark datasets in terms of various performance metrics such as accuracy and training time. The result of the study is very important for comparing or testing new models. A novel multi-source domain adaptation framework for fault diagnosis of rotary machinery is also proposed, which aligns the domains in both feature-level and task-level. The proposed framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Besides, the model can be easily reduced to a single-source domain adaptation problem. Also, the model can be readily updated to unsupervised domain adaptation problems in other fields such as image classification and image segmentation. Further, the proposed model is modified with a novel conditional weighting mechanism that aligns the class-conditional probability of the domains and reduces the effect of irrelevant source domain which is a critical issue in multi-source domain adaptation algorithms. The experimental verification results show the superiority of the proposed framework over state-of-the-art multi-source domain-adaptation models

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

Exploiting gan as an oversampling method for imbalanced data augmentation with application to the fault diagnosis of an industrial robot

O diagnóstico inteligente de falhas baseado em aprendizagem máquina geralmente requer um conjunto de dados balanceados para produzir um desempenho aceitável. No entanto, a obtenção de dados quando o equipamento industrial funciona com falhas é uma tarefa desafiante, resultando frequentemente num desequilíbrio entre dados obtidos em condições nominais e com falhas. As técnicas de aumento de dados são das abordagens mais promissoras para mitigar este problema. Redes adversárias generativas (GAN) são um tipo de modelo generativo que consiste de um módulo gerador e de um discriminador. Por meio de aprendizagem adversária entre estes módulos, o gerador otimizado pode produzir padrões sintéticos que podem ser usados para amumento de dados. Investigamos se asGANpodem ser usadas como uma ferramenta de sobre amostra- -gem para compensar um conjunto de dados desequilibrado em uma tarefa de diagnóstico de falhas num manipulador robótico industrial. Realizaram-se uma série de experiências para validar a viabilidade desta abordagem. A abordagem é comparada com seis cenários, incluindo o método clássico de sobre amostragem SMOTE. Os resultados mostram que a GAN supera todos os cenários comparados. Para mitigar dois problemas reconhecidos no treino das GAN, ou seja, instabilidade de treino e colapso de modo, é proposto o seguinte. Propomos uma generalização da GAN de erro quadrado médio (MSE GAN) da Wasserstein GAN com penalidade de gradiente (WGAN-GP), referida como VGAN (GAN baseado numa matriz V) para mitigar a instabilidade de treino. Além disso, propomos um novo critério para rastrear o modelo mais adequado durante o treino. Experiências com o MNIST e no conjunto de dados do manipulador robótico industrial mostram que o VGAN proposto supera outros modelos competitivos. A rede adversária generativa com consistência de ciclo (CycleGAN) visa lidar com o colapso de modo, uma condição em que o gerador produz pouca ou nenhuma variabilidade. Investigamos a distância fatiada de Wasserstein (SWD) na CycleGAN. O SWD é avaliado tanto no CycleGAN incondicional quanto no CycleGAN condicional com e sem mecanismos de compressão e excitação. Mais uma vez, dois conjuntos de dados são avaliados, ou seja, o MNIST e o conjunto de dados do manipulador robótico industrial. Os resultados mostram que o SWD tem menor custo computacional e supera o CycleGAN convencional.Machine learning based intelligent fault diagnosis often requires a balanced data set for yielding an acceptable performance. However, obtaining faulty data from industrial equipment is challenging, often resulting in an imbalance between data acquired in normal conditions and data acquired in the presence of faults. Data augmentation techniques are among the most promising approaches to mitigate such issue. Generative adversarial networks (GAN) are a type of generative model consisting of a generator module and a discriminator. Through adversarial learning between these modules, the optimised generator can produce synthetic patterns that can be used for data augmentation. We investigate whether GAN can be used as an oversampling tool to compensate for an imbalanced data set in an industrial robot fault diagnosis task. A series of experiments are performed to validate the feasibility of this approach. The approach is compared with six scenarios, including the classical oversampling method (SMOTE). Results show that GAN outperforms all the compared scenarios. To mitigate two recognised issues in GAN training, i.e., instability and mode collapse, the following is proposed. We proposed a generalization of both mean sqaure error (MSE GAN) and Wasserstein GAN with gradient penalty (WGAN-GP), referred to as VGAN (the V-matrix based GAN) to mitigate training instability. Also, a novel criterion is proposed to keep track of the most suitable model during training. Experiments on both the MNIST and the industrial robot data set show that the proposed VGAN outperforms other competitive models. Cycle consistency generative adversarial network (CycleGAN) is aiming at dealing with mode collapse, a condition where the generator yields little to none variability. We investigate the sliced Wasserstein distance (SWD) for CycleGAN. SWD is evaluated in both the unconditional CycleGAN and the conditional CycleGAN with and without squeeze-and-excitation mechanisms. Again, two data sets are evaluated, i.e., the MNIST and the industrial robot data set. Results show that SWD has less computational cost and outperforms conventional CycleGAN

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

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

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

Wind Power Forecasting Methods Based on Deep Learning: A Survey

Accurate wind power forecasting in wind farm can effectively reduce the enormous impact on grid operation safety when high permeability intermittent power supply is connected to the power grid. Aiming to provide reference strategies for relevant researchers as well as practical applications, this paper attempts to provide the literature investigation and methods analysis of deep learning, enforcement learning and transfer learning in wind speed and wind power forecasting modeling. Usually, wind speed and wind power forecasting around a wind farm requires the calculation of the next moment of the definite state, which is usually achieved based on the state of the atmosphere that encompasses nearby atmospheric pressure, temperature, roughness, and obstacles. As an effective method of high-dimensional feature extraction, deep neural network can theoretically deal with arbitrary nonlinear transformation through proper structural design, such as adding noise to outputs, evolutionary learning used to optimize hidden layer weights, optimize the objective function so as to save information that can improve the output accuracy while filter out the irrelevant or less affected information for forecasting. The establishment of high-precision wind speed and wind power forecasting models is always a challenge due to the randomness, instantaneity and seasonal characteristics

Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks

Machine learning techniques have been successfully applied in intelligent fault diagnosis of rolling bearings in recent years. However, in the real world industrial application, the dissimilarity of data due to changes in the working conditions and data acquisition environment often cause a poor performance of the existing fault diagnosis methods. Consequently, to address these inadequacies, this paper developed a novel method by integrating the Convolutional Neural Networks (CNNs) with the Variational Mode Decomposition (VMD) algorithms. Named as “Variational Mode Decomposition with Deep Convolutional Neural Networks (VMD-DCNNs)”, the method, in an end-to-end way, directly processes raw vibration signals without artificial experiences and manual intervention to realize the fault diagnosis of rolling bearings. In addition, the CNN technique is used to extract features from each Intrinsic Mode Function (IMF) in order to address the deficiency in extracting features from a single source and to achieve an effective and efficient fault diagnosis of rolling bearings under different environments and states. The value of parameter K of the VMD-DCNNs model is optimized by considering time complexity and generalization ability of the model. Lastly, bearing experiments are conducted to verify the superiority of the VMD-DCNNs in diagnosing fault under different conditions. The visualizations of the signals in the convolutional layer explain the reasonability in selecting the value of parameter K and they also indicate that the translational invariances in a raw IMF component have been learned by the VMD-DCNNs model

On Bayesian Networks for Structural Health and Condition Monitoring

The first step in data-driven approaches to Structural Health Monitoring (SHM) is that of damage detection. This is a problem that has been well studied in laboratory conditions. Yet, SHM remains an academic topic, not yet widely implemented in industry. One of the main reasons for this is arguably the difficulty in dealing with Environmental and Operational Variabilities (EOVs), which have a tendency to influence damage-sensitive features in ways similar to damage itself. A large number of the methods developed for SHM applications make use of linear Gaussian models for various tasks including dimensionality reduction, density estimation and system identification. A wide range of linear Gaussian models can be formulated as special cases of a general class of probabilistic graphical models, or Bayesian networks. The work presented here discusses how Bayesian networks can be used systematically to approach different types of damage detection problems, through their likelihood function. A likelihood evaluates the probability that an observation belongs to a particular model. If this model correctly captures the undamaged state of the system, then a likelihood can be used as a novelty index, which can point to the presence of damage. Likelihood functions can be systematically exploited for damage detection purposes across the vast range of linear Gaussian models. One of the key benefits of this fact is that simple models can easily be extended to mixtures of linear Gaussian models. It is shown how this approach can be effective in dealing with operational and environmental variabilities. This thesis thus provides a point of view on performing novelty detection under this wide class of models systematically with their likelihood functions. Models that are typically used for other purposes can become powerful novelty detectors in this view. The relationship between Principal Component Analysis (PCA) and Kalman filters is a good example of this. Under the graphical model perspective these two models are a simple variation of each other, where they model data with and without time dependence. Provided these models are trained with representative data from a non-damaged system, their likelihood function presents a useful novelty index. Their limitation to modelling linear Gaussian data can be overcome through the mixture modelling interpretation. Through graphical models, this is a straightforward extension, but one that retains a probabilistic interpretation. The impact of this interpretation is that environmental and operational variability, as well as potential nonlinearity, in SHM features can be captured by these models. Even though the interpretation changes depending on the model, the likelihood function can consistently be used as a damage indicator, throughout models like Gaussian mixtures, PCA, Factor Analysis, Autoregressive models, Kalman filters and switching Kalman filters. The work here focuses around these models. There are various ways in which these models can be used, but here the focus is narrowed to exploring them as novelty detectors, and showing their application in different contexts. The context in this case refers to different types of SHM data and features, as this could be either vibration, acoustics, ultrasound, performance metrics, etc. %The thesis divides into three main sections. The first presents an overview and scope, with introductions to SHM data, machine learning and the use of likelihood functions for novelty detection. This thesis provides a discussion on the theoretical background for probabilistic graphical models, or Bayesian networks. Separate chapters are dedicated to the discussion of Bayesian networks to model static and dynamic data (with and without temporal dependencies, respectively). Furthermore, three different application examples are presented to demonstrate the use of likelihood function inference for damage detection. These systems are a simulated mass-spring-damper system, with varying stiffness in its non-damaged condition, and with a cubic spring nonlinearity. This system presents a challenge from the point of view of the characterisation of the changing environment in terms of global stiffness and excitation energy. It is shown how mixtures of PCA models can be used to tackle this problem if frequency domain features are used, and mixtures of linear dynamical systems (Kalman filters) can be used to successfully characterise the baseline undamaged system and to identify the presence of damage directly from time domain measurements. Another case study involves the detection of damage on the Z-24 bridge. This is a well-studied problem in SHM research, and it is of interest due to the nonlinear stiffness effect due to temperature changes. The features used here are the first four natural frequencies of the bridge. It is demonstrated how a Gaussian mixture model can characterise the undamaged condition, and its likelihood is able to accurately predict the presence of damage. The third case study involves the prediction of various stages of damage on a wind turbine bearing. This is an experimental laboratory investigation - and the problem is also tackled with a Gaussian mixture model. This problem is of interest because the lowest damage level seeded in the bearing was subsurface yield. This is of great relevance to the wind turbine community, as detecting this level of damage is currently not feasible. Features from Acoustic Emission (AE) measurements were used to train a Gaussian mixture model. It is shown that the likelihood function of this model can correctly predict the presence of damage