The monitoring of rolling element bearing is indexed as a critical task for condition-based maintenance in various industrial applications. It allows avoiding unscheduled maintenance operations while decreasing their cost. For this purpose, various methodologies were developed to ensure accurate and efficient monitoring. In this context, this paper proposes an approach for bearing fault early diagnosis based on the variational mode decomposition (VMD), used as a notch filter for dominant mode cancellation, and a machine learning approach, namely the onedimensional convolution neural network (1D-CNN), for detection and diagnosis purposes. Specifically, the proposed approach first performs features extraction using VMD for fault detection, and then triggers to multi-scale features extraction using CNN convolution and pooling layers for classification and diagnosis.The proposed bearing fault detection and diagnosis approach is evaluated, in terms of robustness and performances, using the well-known Case Western Reserve University experimental dataset. In addition, performances are evaluated versus wellestablished demodulation techniques, in terms of fault detection, and machine learning strategies, in terms of fault diagnosis. The achieved results show that the proposed VMD notch filter-based 1D-CNN approach is clearly promising for bearing degradation monitoring.
Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.
Life prediction and health assessment of cutting tools is a challenging problem in industrial manufacturing, and plays an important role for Prognostic activities. The degradation of this tool can cause significant economic losses and risks for machine users. However, due to the random nature of system degradation and the sensitivity of various features characterizing cutter degradation, may vary considerably under different operation conditions. This paper propose a new data driven approach for tool condition monitoring, based on kernel smoothing density to model the random phenomena of degradation progression. The health assessment is insured using one directional neural network to extract useful information’s. Deep learning allows relating between extracted features and health indicator progression, and estimate remaining useful life. This methodology is evaluated using regression metrics and compared with recent frameworks, based on an experimental dataset from PHM Data Challenge 2010. The obtained results shows that the proposed approach can effectively predict the tool wear and obtain higher prediction accuracy than other comparison models.
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