Author
R. Jegadeeshwaran
Bio: R. Jegadeeshwaran is an academic researcher from VIT University. The author has contributed to research in topics: Condition monitoring & Hydraulic brake. The author has an hindex of 9, co-authored 37 publications receiving 348 citations.
Papers
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TL;DR: On-line condition monitoring by using machine learning approach is proposed in this paper as a possible solution to hydraulic brakes problems by using the decision tree algorithm.
164 citations
TL;DR: A machine learning algorithm using vibration monitoring is proposed as a possible solution to the problem of monitoring the condition of hydraulic brakes by using the vibration characteristics.
56 citations
TL;DR: An ML-based approach to monitor the multipoint tool insert health is presented and various tool conditions were categorized using six different ‘supervised-tree-based’ algorithms and a comparative study is presented to find the best possible classifier.
51 citations
TL;DR: In this research, the performance of a Clonal Selection Classification Algorithm (CSCA) for brake fault diagnosis has been reported and the classification accuracy of such artificial intelligence technique has been compared with other machine learning approaches and discussed.
32 citations
TL;DR: The application of wavelets has been investigated for diagnosing the faults on a hydraulic brake system of a light motor vehicle using the vibration signals acquired from a brake test setup through a piezoelectric type accelerometer.
Abstract: In this study, the application of wavelets has been investigated for diagnosing the faults on a hydraulic brake system of a light motor vehicle using the vibration signals acquired from a brake tes...
30 citations
Cited by
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Journal Article•
9,185 citations
TL;DR: A review and roadmap to systematically cover the development of IFD following the progress of machine learning theories and offer a future perspective is presented.
1,173 citations
Journal Article•
631 citations
TL;DR: An effective and reliable deep learning method known as stacked denoising autoencoder (SDA), which is shown to be suitable for certain health state identifications for signals containing ambient noise and working condition fluctuations, is investigated.
591 citations
TL;DR: In this article, a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery is presented, which incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy.
Abstract: This paper presents a convolutional neural network (CNN) based approach for fault diagnosis of rotating machinery. The proposed approach incorporates sensor fusion by taking advantage of the CNN structure to achieve higher and more robust diagnosis accuracy. Both temporal and spatial information of the raw data from multiple sensors is considered during the training process of the CNN. Representative features can be extracted automatically from the raw signals. It avoids manual feature extraction or selection, which relies heavily on prior knowledge of specific machinery and fault types. The effectiveness of the developed method is evaluated by using datasets from two types of typical rotating machinery, roller bearings, and gearboxes. Compared with traditional approaches using manual feature extraction, the results show the superior diagnosis performance of the proposed method. The present approach can be extended to fault diagnosis of other machinery with various types of sensors due to its end to end feature learning capability.
449 citations