Author
S. Devendiran
Bio: S. Devendiran is an academic researcher from VIT University. The author has contributed to research in topics: Machining & Tool wear. The author has an hindex of 7, co-authored 20 publications receiving 126 citations.
Topics: Machining, Tool wear, Inconel, Surface roughness, Machinability
Papers
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TL;DR: A new intelligent methodology in bearing condition diagnosis analysis has been proposed to predict the status of rolling bearing based on vibration signals by multi class support vector machine (MSVM), a classification algorithm.
42 citations
TL;DR: Signal processing categorized to time-frequency domain such as continues wavelet transform is used in the proposed work for statistical feature extraction and feature selection method is used for selecting the extensive useful features among the extracted features to reduce the processing time.
25 citations
TL;DR: In this paper, the machinability study under MQL on CNC turning of 617 alloys by AlTiN PVD carbide cutting inserts has been examined using the Taguchi L9 orthogonal array parameter design and response surface optimization.
23 citations
TL;DR: Hastelloy X (Hast-X) is used as a critical component in industries of aerospace, chemical, gas turbine, and marine owing to its excellent properties as mentioned in this paper.
Abstract: Hastelloy X (Hast-X) is used as a critical component in industries of aerospace, chemical, gas turbine, and marine owing to its excellent properties. Minimum quantity lubrication (MQL) found has a ...
18 citations
TL;DR: In this paper, a comparative performance of input-parameters on cutting force, roughness, tool wear, and chip morphology of sustainable conditions (dry and MQL: minimum quantity lubrication) on Nimonic 90 alloy was investigated.
16 citations
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01 Jan 2016
300 citations
TL;DR: This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings and serves as a guidemap for researchers in the field of early fault diagnosis.
Abstract: Rotating machinery is widely applied in various types of industrial applications. As a promising field for reliability of modern industrial systems, early fault diagnosis (EFD) techniques have attracted increasing attention from both academia and industry. EFD is critical to provide appropriate information for taking necessary maintenance actions and thereby prevent severe failures and reduce financial losses. A massive amounts of research work has been conducted in last two decades to develop EFD techniques. This paper reviews and summarizes the research works on EFD of gears, rotors, and bearings. The main purpose of this paper is to serve as a guidemap for researchers in the field of early fault diagnosis. After a brief introduction of early fault diagnosis techniques, the applications of EFD of rotating machine are reviewed in two aspects: fault frequency-based methods and artificial intelligence-based methods. Finally, a summary and some new research prospects are discussed.
131 citations
TL;DR: A multi-class support vector machine (SVM) based process supervision and fault diagnosis scheme is proposed to predict the status of the Tennessee Eastman (TE) Process to demonstrate the effectiveness of the proposed SVM integrated GS-PCA fault diagnosis approach.
129 citations
TL;DR: A review of deep learning challenges related to machinery fault detection and diagnosis systems and the potential for future work on deep learning implementation in FDD systems is briefly discussed.
Abstract: 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.
127 citations
TL;DR: The recent R&D trends in the basic research field of machinery fault diagnosis is reviewed in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics.
Abstract: Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.
91 citations