Deep Learning for predictive maintenance

Recently, with the appearance of Industry 4.0 (I4.0), machine learning (ML) within artificial intelligence (AI), industrial Internet of things (IIoT) and cyber-physical system (CPS) have accelerated the development of a data-orientated applications such as predictive maintenance (PdM). PdM applied to asset-dependent industries has led to operational cost savings, productivity improvements and enhanced safety management capabilities. In addition, predictive maintenance strategies provide useful information concerning the source of the failure or malfunction, reducing unnecessary maintenance operations. The concept of prognostics and health management (PHM) has appeared as a predictive maintenance process. PHM has become an unavoidable tendency in smart manufacturing to offer a reliable solution for handling industrial equipment’s health status. This later requires efficient and effective system health monitoring methods, including processing and analysing massive machinery data to detect anomalies and perform diagnosis and prognosis. Prognostics is considered a key PHM process with capabilities for predicting future states, mainly based on predicting the residual lifetime during which a machine can perform its intended function, i.e., estimating the remaining useful life (RUL) of a system. The prognostic research domain is far from being mature, which is still new and explains the various challenges that must be addressed. Therefore, the work presented in this thesis will mainly focus on the prognostic of monitored machinery from an RUL estimation point of view using Deep Learning (DL) algorithms. Capitalising on the recent success of the DL, this dissertation introduces methods and algorithms dedicated to predictive maintenance. We focused on improving the performance of aero-engine prognostic, particularly in estimating an accurate RUL using ensemble learning and deep learning. To this end, two contributions have been proposed, and the results obtained were validated by an extensive comparative analysis using public C-MAPSS turbofan engine benchmark datasets. The first contribution, for RUL predictions, we proposed two-hybrid methods based on the promising DL architectures to leverage the power of the multimodal and hybrid deep neural network in order to capture various information at different time intervals and ultimately achieve more accurate RUL predictions. The proposed end-to-end deep architectures jointly optimise the feature reduction and RUL prediction steps in a hierarchical manner, intending to achieve data representation in low dimensionality and minimal variable redundancy while preserving critical asset degradation information with minimal preprocessing effort. The second contribution, in a practical situation, RUL is usually affected by uncertainty. Therefore, we proposed an innovative RUL estimation strategy that assesses degrading machinery’s health status (provides the probabilities of system failure in different time windows) and provides the prediction of RUL window. Keywords: Prognostics and Health Management (PHM), Remaining useful life (RUL), Predictive Maintenance (PdM), C-MAPSS dataset, Ensemble learning, Deep learnin

Breast cancer classification using machine learning techniques: a comparative study

Background: The second leading deadliest disease affecting women worldwide, after  lung cancer, is breast cancer. Traditional approaches for breast cancer diagnosis suffer from time consumption and some human errors in classification. To deal with this problems, many research works based on machine learning techniques are proposed.  These approaches show  their effectiveness in data classification in many fields, especially in healthcare.      Methods: In this cross sectional study, we conducted a practical comparison between the most used machine learning algorithms in the literature. We applied kernel and linear support vector machines, random forest, decision tree, multi-layer perceptron, logistic regression, and k-nearest neighbors for breast cancer tumors classification.  The used dataset is  Wisconsin diagnosis Breast Cancer. Results: After comparing the machine learning algorithms efficiency, we noticed that multilayer perceptron and logistic regression gave  the best results with an accuracy of 98% for breast cancer classification.       Conclusion: Machine learning approaches are extensively used in medical prediction and decision support systems. This study showed that multilayer perceptron and logistic regression algorithms are  performant  ( good accuracy specificity and sensitivity) compared to the  other evaluated algorithms

RUL Estimation Enhancement using Hybrid Deep Learning Methods

The turbofan engine is one of the most critical aircraft components. Its failure may introduce unwanted downtime, expensive repair, and affect safety performance. Therefore, It is essential to accurately detect upcoming failures by predicting the future behavior health state of turbofan engines as well as its Remaining Useful Life. The use of deep learning techniques to estimate Remaining Useful Life has seen a growing interest over the last decade. However, hybrid deep learning methods have not been sufficiently explored yet by researchers. In this paper, we proposed two-hybrid methods combining Convolutional Auto-encoder (CAE), Bi-directional Gated Recurrent Unit (BDGRU), Bi-directional Long-Short Term Memory (BDLSTM), and Convolutional Neural Network (CNN) to enhance the RUL estimation. The results indicate that the hybrid methods exhibit the most reliable RUL prediction accuracy and significantly outperform the most robust predictions in the literature

An Improved Model for Breast Cancer Diagnosis by Combining PCA and Logistic Regression Techniques

Abstract: Breast cancer is weighed one of the most life-threatening illnesses confronting women. It happens when the multiplication of cells in breast tissue is uncontrollable. Several studies have been performed in the healthcare field for early breast cancer diagnosis. However, traditional methods can generate incomplete or misleading outcomes. To overcome these limitations, computer-aided diagnosis (CAD) systems are extensively exploited in the healthcare domain. It is designed to improve accuracy, decrease complexity, and reduce misclassification costs. The goal of this study is to present a breast cancer CAD system based on combining the Principal Component Analysis (PCA) method for feature reduction and Logistic Regression (LR) for BC tumors classification. The experiments have been conducted on Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Original Breast Cancer (WOBC) datasets from UCI repository using different training and testing subsets. Moreover, we carried out extensive comparisons of our approach with other existing approaches. Multiple metrics like precision, F1 score, recall, accuracy, and Area Under Curve (AUC) were used in this study. Experimental results indicate that the proposed approach records a remarkable performance rate with an accuracy of 1.00 and 0.98 for WDBC and WOBC respectively and outperforms the previous works by decreasing the number of features, improving the data quality, and reducing the response time.16 página

An overview on the deep learning based prognostic

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RUL Prediction Using a Fusion of Attention-Based Convolutional Variational AutoEncoder and Ensemble Learning Classifier

International audiencePredicting the remaining useful life(RUL) is a critical step before the decision-making process anddeveloping mainte- nance strategies. As a result, it is frequentlyimpacted by uncer- tainty in a practical context and may causeissues. This article proposes a new hybrid deep architecture thatpredicts when an in-service machine will fail to overcome thelatter problem, al- lowing for an improved data analysis anddimensionality reduc- tion capability providing better spatialdistributions of features and increasing interpretability. A deepconvolutional variational autoencoder with an attention mechanism(ACVAE) has been de- veloped and tested using the aero-engineC-MAPSS dataset. We defined two adapted threshold settings (α1, α2)by analyzing the spatial distribution and minimizing theoverlapping area between the degradation classes. To reduce theconflict zone, we used the soft voting classifier. The performanceof our visual explainable deep learning model has reached a higherlevel of accuracy compared with previous existing models

Unsupervised Feature Reduction Techniques with Bidirectional GRU Neural Network for Aircraft Engine RUL Estimation

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Leveraging the Power of the Combination of CNN and Bi-Directional LSTM Networks for Aircraft Engine RUL Estimation

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Hybrid Architecture of Deep Convolutional Variational Auto-encoder for Remaining useful Life Prediction

International audienceThe remaining useful life prediction is a key element in decision-making and maintenance strategies development.Therefore, in practical situation, it is usually affected by uncertainty. The aim of this work is hence to propose a deeplearning method which predicts when an in-service machine will fail to overcome the latter problem. It is based ondeep convolutional variational autoencoder (CVAE). The proposed approach is validated using the C-MAPSS datasetof the aero-engine. The model’s classification performance has reached a superior accuracy compared to existingmodels and it is used for machine failure prediction in different time windows