Analytics of Sequential Time Data from Physical Assets

RÉSUMÉ: Avec l’avancement dans les technologies des capteurs et de l’intelligence artificielle, l'analyse des données est devenue une source d’information et de connaissance qui appuie la prise de décisions dans l’industrie. La prise de ces décisions, en se basant seulement sur l’expertise humaine n’est devenu suffisant ou souhaitable, et parfois même infaisable pour de nouvelles industries. L'analyse des données collectées à partir des actifs physiques vient renforcer la prise de décisions par des connaissances pratiques qui s’appuient sur des données réelles. Ces données sont utilisées pour accomplir deux tâches principales; le diagnostic et le pronostic. Les deux tâches posent un défi, principalement à cause de la provenance des données et de leur adéquation avec l’exploitation, et aussi à cause de la difficulté à choisir le type d'analyse. Ce dernier exige un analyste ayant une expertise dans les déférentes techniques d’analyse de données, et aussi dans le domaine de l’application. Les problèmes de données sont dus aux nombreuses sources inconnues de variations interagissant avec les données collectées, qui peuvent parfois être dus à des erreurs humaines. Le choix du type de modélisation est un autre défi puisque chaque modèle a ses propres hypothèses, paramètres et limitations. Cette thèse propose quatre nouveaux types d'analyse de séries chronologiques dont deux sont supervisés et les deux autres sont non supervisés. Ces techniques d'analyse sont testées et appliquées sur des différents problèmes industriels. Ces techniques visent à minimiser la charge de choix imposée à l'analyste. Pour l’analyse de séries chronologiques par des techniques supervisées, la prédiction de temps de défaillance d’un actif physique est faite par une technique qui porte le nom de ‘Logical Analysis of Survival Curves (LASC)’. Cette technique est utilisée pour stratifier de manière adaptative les courbes de survie tout au long d’un processus d’inspection. Ceci permet une modélisation plus précise au lieu d'utiliser un seul modèle augmenté pour toutes les données. L'autre technique supervisée de pronostic est un nouveau réseau de neurones de type ‘Long Short-Term Memory (LSTM) bidirectionnel’ appelé ‘Bidirectional Handshaking LSTM (BHLSTM)’. Ce modèle fait un meilleur usage des séquences courtes en faisant un tour de ronde à travers les données. De plus, le réseau est formé à l'aide d'une nouvelle fonction objective axée sur la sécurité qui force le réseau à faire des prévisions plus sûres. Enfin, étant donné que LSTM est une technique supervisée, une nouvelle approche pour générer la durée de vie utile restante (RUL) est proposée. Cette technique exige la formulation des hypothèses moins importantes par rapport aux approches précédentes. À des fins de diagnostic non supervisé, une nouvelle technique de classification interprétable est proposée. Cette technique est intitulée ‘Interpretable Clustering for Rule Extraction and Anomaly Detection (IC-READ)’. L'interprétation signifie que les groupes résultants sont formulés en utilisant une logique conditionnelle simple. Cela est pratique lors de la fourniture des résultats à des non-spécialistes. Il facilite toute mise en oeuvre du matériel si nécessaire. La technique proposée est également non paramétrique, ce qui signifie qu'aucun réglage n'est requis. Cette technique pourrait également être utiliser dans un contexte de ‘one class classification’ pour construire un détecteur d'anomalie. L'autre technique non supervisée proposée est une approche de regroupement de séries chronologiques à plusieurs variables de longueur variable à l'aide d'une distance de type ‘Dynamic Time Warping (DTW)’ modifiée. Le DTW modifié donne des correspondances plus élevées pour les séries temporelles qui ont des tendances et des grandeurs similaires plutôt que de se concentrer uniquement sur l'une ou l'autre de ces propriétés. Cette technique est également non paramétrique et utilise la classification hiérarchique pour regrouper les séries chronologiques de manière non supervisée. Cela est particulièrement utile pour décider de la planification de la maintenance. Il est également montré qu'il peut être utilisé avec ‘Kernel Principal Components Analysis (KPCA)’ pour visualiser des séquences de longueurs variables dans des diagrammes bidimensionnels.---------- ABSTRACT: Data analysis has become a necessity for industry. Working with inherited expertise only has become insufficient, expensive, not easily transferable, and mostly unavailable for new industries and facilities. Data analysis can provide decision-makers with more insight on how to manage their production, maintenance and personnel. Data collection requires acquisition and storage of observatory information about the state of the different production assets. Data collection usually takes place in a timely manner which result in time-series of observations. Depending on the type of data records available, the type of possible analyses will differ. Data labeled with previous human experience in terms of identifiable faults or fatigues can be used to build models to perform the expert’s task in the future by means of supervised learning. Otherwise, if no human labeling is available then data analysis can provide insights about similar observations or visualize these similarities through unsupervised learning. Both are challenging types of analyses. The challenges are two-fold; the first originates from the data and its adequacy, and the other is selecting the type of analysis which is a decision made by the analyst. Data challenges are due to the substantial number of unknown sources of variations inherited in the collected data, which may sometimes include human errors. Deciding upon the type of modelling is another issue as each model has its own assumptions, parameters to tune, and limitations. This thesis proposes four new types of time-series analysis, two of which are supervised requiring data labelling by certain events such as failure when, and the other two are unsupervised that require no such labelling. These analysis techniques are tested and applied on various industrial applications, namely road maintenance, bearing outer race failure detection, cutting tool failure prediction, and turbo engine failure prediction. These techniques target minimizing the burden of choice laid on the analyst working with industrial data by providing reliable analysis tools that require fewer choices to be made by the analyst. This in turn allows different industries to easily make use of their data without requiring much expertise. For prognostic purposes a proposed modification to the binary Logical Analysis of Data (LAD) classifier is used to adaptively stratify survival curves into long survivors and short life sets. This model requires no parameters to choose and completely relies on empirical estimations. The proposed Logical Analysis of Survival Curves show a 27% improvement in prediction accuracy than the results obtained by well-known machine learning techniques in terms of the mean absolute error. The other prognostic model is a new bidirectional Long Short-Term Memory (LSTM) neural network termed the Bidirectional Handshaking LSTM (BHLSTM). This model makes better use of short sequences by making a round pass through the given data. Moreover, the network is trained using a new safety oriented objective function which forces the network to make safer predictions. Finally, since LSTM is a supervised technique, a novel approach for generating the target Remaining Useful Life (RUL) is proposed requiring lesser assumptions to be made compared to previous approaches. This proposed network architecture shows an average of 18.75% decrease in the mean absolute error of predictions on the NASA turbo engine dataset. For unsupervised diagnostic purposes a new technique for providing interpretable clustering is proposed named Interpretable Clustering for Rule Extraction and Anomaly Detection (IC-READ). Interpretation means that the resulting clusters are formulated using simple conditional logic. This is very important when providing the results to non-specialists especially those in management and ease any hardware implementation if required. The proposed technique is also non-parametric, which means there is no tuning required and shows an average of 20% improvement in cluster purity over other clustering techniques applied on 11 benchmark datasets. This technique also can use the resulting clusters to build an anomaly detector. The last proposed technique is a whole multivariate variable length time-series clustering approach using a modified Dynamic Time Warping (DTW) distance. The modified DTW gives higher matches for time-series that have the similar trends and magnitudes rather than just focusing on either property alone. This technique is also non-parametric and uses hierarchal clustering to group time-series in an unsupervised fashion. This can be specifically useful for management to decide maintenance scheduling. It is shown also that it can be used along with Kernel Principal Components Analysis (KPCA) for visualizing variable length sequences in two-dimensional plots. The unsupervised techniques can help, in some cases where there is a lot of variation within certain classes, to ease the supervised learning task by breaking it into smaller problems having the same nature

Recurrent neural networks and its variants in Remaining Useful Life prediction

Data-driven techniques, especially artificial intelligence (AI) based deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector because of the rapid growth of the industrial Internet of Things (IoT) and Big Data. Tremendous researches of DL techniques have been applied in machine health monitoring, but still very limited works focus on the application of DL on the Remaining Useful Life (RUL) prediction. Precise RUL prediction can significantly improve the reliability and operational safety of industrial components or systems, avoid fatal breakdown and reduce the maintenance costs. This paper reviews and compares the state-of-the-art DL approaches for RUL prediction focusing on Recurrent Neural Networks (RNN) and its variants. It has been observed from the results for a publicly available dataset that Long Short-Term Memory (LSTM) networks and Gated Recurrent Unit (GRU) networks outperform the basic RNNs, and the number of the network layers affects the performance of the prediction

A Hybrid Prognostic Approach Based on Deep Learning for the Degradation Prediction of Machinery

Remaining useful life (RUL) prediction is of great significance for prognostic and health management (PHM) as it can achieve more reliable and effective maintenance strategies. With the advances in the field of deep learning, data-driven methods have provided promising prognostic prediction results. Hence, this research presents a data-driven prognostic approach based on deep learning models for predicting the RUL of mechanical systems effectively. Multiple separable convolution layers, a bidirectional Long Short-Term Memory (LSTM) layer, and fully-connected layers (FCL) are included in the proposed network, named the SC-BLSTM, to accomplish more accurate prognostic prediction from the raw degradation data acquired by different sensors. The proposed SC-BLSTM approach aims to learn complex and nonlinear features from the input data and capture temporal dependencies from the learned features. The presented approach in this research is tested and verified on the degradation data of turbofan engines (C-MAPSS dataset) from NASA. The result demonstrated that the SC-BLSTM is able to achieve more effective RUL prediction compared with some existing prognostic models

Innovative actuator fault identification based on back electromotive force reconstruction

The ever increasing adoption of electrical power as secondary form of on-board power is leading to an increase in the usage of electromechanical actuators (EMAs). Thus, in order to maintain an acceptable level of safety and reliability, innovative prognostics and diagnostics methodologies are needed to prevent performance degradation and/or faults propagation. Furthermore, the use of effective prognostics methodologies carries several benefits, including improved maintenance schedule capability and relative cost decrease, better knowledge of systems health status and performance estimation. In this work, a novel, real-time approach to EMAs prognostics is proposed. The reconstructed back electromotive force (back-EMF), determined using a virtual sensor approach, is sampled and then used to train an artificial neural network (ANN) in order to evaluate the current system status and to detect possible coils partial shorts and rotor imbalances

Degradation Vector Fields with Uncertainty Considerations

The focus of this work is on capturing uncertainty in remaining useful life (RUL) estimates for machinery and constructing some latent dynamics that aid in interpreting those results. This is primarily achieved through sequential deep generative models known as Dynamical Variational Autoencoders (DVAEs). These allow for the construction of latent dynamics related to the RUL estimates while being a probabilistic model that can quantify the uncertainties of the estimates

Multi-scale remaining useful life prediction using long short-term memory

Predictive maintenance based on performance degradation is a crucial way to reduce maintenance costs and potential failures in modern complex engineering systems. Reliable remaining useful life (RUL) prediction is the main criterion for decision-making in predictive maintenance. Conventional model-based methods and data-driven approaches often fail to achieve an accurate prediction result using a single model for a complex system featuring multiple components and operational conditions, as the degradation pattern is usually nonlinear and time-varying. This paper proposes a novel multi-scale RUL prediction approach adopting the Long Short-Term Memory (LSTM) neural network. In the feature engineering phase, Pearson’s correlation coefficient is applied to extract the representative features, and an operation-based data normalisation approach is presented to deal with the cases where multiple degradation patterns are concealed in the sensor data. Then, a three-stage RUL target function is proposed, which segments the degradation process of the system into the non-degradation stage, the transition stage, and the linear degradation stage. The classification of these three stages is regarded as the small-scale RUL prediction, and it is achieved through processing sensor signals after the feature engineering using a novel LSTM-based binary classification algorithm combined with a correlation method. After that, a specific LSTM-based predictive model is built for the last two stages to produce a large-scale RUL prediction. The proposed approach is validated by comparing it with several state-of-the-art techniques based on the widely used C-MAPSS dataset. A significant improvement is achieved in RUL prediction performance in most subsets. For instance, a 40% reduction is achieved in Root Mean Square Error over the best existing method in subset FD001. Another contribution of the multi-scale RUL prediction approach is that it offers more degree of flexibility of prediction in the maintenance strategy depending on data availability and which degradation stage the system is in

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