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Piyush Shakya

Bio: Piyush Shakya is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Condition monitoring & Rolling-element bearing. The author has an hindex of 6, co-authored 12 publications receiving 134 citations. Previous affiliations of Piyush Shakya include Indian Institute of Technology Delhi.

Papers
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Journal ArticleDOI
TL;DR: In this article, the in-process signals collected using various sensors attached to a cylindrical grinding machine such as Accelerometer and Power are processed, and their features are correlated with a surface finish parameter.

1 citations

Journal ArticleDOI
TL;DR: In this paper , a new method, the Pruned Exact Linear Time, was proposed for identifying the incipient faults and following damage states in the bearing, which can identify whether the bearing is in a mild failure state or medium failure state.
Abstract: Rolling element bearing is a crucial component in rotating machines. The existence of faults in the bearing causes sudden failures, resulting in catastrophic failure of machines. Incipient fault detection and health state assessment are the essential tasks in condition-based maintenance to avoid machine failures. This paper proposes a new method, the Pruned Exact Linear Time, for identifying the incipient faults and following damage states in the bearing. When a fault initiates in the bearing, there is an increment in the vibration response. This increment can be quantified by the degradation indicator computed from the vibration signal. The Variational Mode Decomposition technique is used to de-noise the vibration signals. Various statistical features are derived from the de-noised signal, and the best feature subset is chosen by the Recursive Feature Elimination method. Then, the significant bearing life degradation indicator is computed using the Reconstruction Independent Component Analysis method by fusing selected features. A novel index is formulated for computing the percentage of failure, which can identify whether the bearing is in a mild failure state or medium failure state. The efficiency of the proposed framework is demonstrated using experimental bearing datasets.
01 Jan 2015
TL;DR: In this paper, a data fusion based methodology for online detection of health status and defect type in the bearing of a grinding machine is presented, which takes into account the correlation among all the damage identification parameters considered.
Abstract: Application of a data fusion based methodology for online detection of health status and defect type in the bearing of a grinding machine is presented. In practice, knowing the exact defect status and type is infeasible. Information regarding the current health status and defect type of a bearing may help in building prognosis models. As the proposed detection methodology is based on data fusion, dependence on a single damage identification parameter is obviated. The fused data parameter takes into account the correlation among all the damage identification parameters considered. Diagnosis of a bearing with naturally induced and progressed defect may have multiple complexities. Typically used condition monitoring parameters, such as R.M.S. and peak may not have monotonically increasing trends. In the case of natural defects, one type of the defect may be prominent in the initial phase and later on, another type of defect may outgrow the first one or both may exist simultaneously. The methodology is verified with the help of a dataset acquired from a naturally induced and progressed defect on an accelerated test rig. The bearing is dismantled after the experiment to confirm the defect type identified through the method.
Proceedings ArticleDOI
09 Nov 2022
TL;DR: In this article , a wrapper-based feature selection method was used to select the significant feature subset and Local tangent space alignment method was employed to extract health indicators by fusing selected features.
Abstract: Bearing is an essential component in the rotatory machine. Its failure causes the sudden failure of industrial machines, which increases downtime and productivity loss. Early fault detection involves finding the fault initiation point where bearing enters from healthy to a failure state. Detecting the fault initiation in the bearing is essential to avoid deteriorating conditions and catastrophic failure of the machines. Extracting significant health indicators from bearing condition monitoring data is vital for detecting the accurate incipient fault. Initially, various features are extracted using signal processing techniques. The wrapper-based feature selection method is used to select the significant feature subset. Then, the Local tangent space alignment method is used to extract health indicators by fusing selected features. Finally, Hypothesis testing is used to detect the early fault in bearing, and the Support vector machine method is used to distinguish the healthy and fault states. The proposed methodology is verified using bearing accelerated life test data.

Cited by
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Journal ArticleDOI
Yaguo Lei1, Naipeng Li1, Liang Guo1, Ningbo Li1, Tao Yan1, Jing Lin1 
TL;DR: A review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction, which provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.

1,116 citations

Journal ArticleDOI
TL;DR: A thorough review of vibration-based bearing and gear health indicators constructed from mechanical signal processing, modeling, and machine learning is presented and provides a basis for predicting the remaining useful life of bearings and gears.
Abstract: Prognostics and health management is an emerging discipline to scientifically manage the health condition of engineering systems and their critical components. It mainly consists of three main aspects: construction of health indicators, remaining useful life prediction, and health management. Construction of health indicators aims to evaluate the system’s current health condition and its critical components. Given the observations of a health indicator, prediction of the remaining useful life is used to infer the time when an engineering systems or a critical component will no longer perform its intended function. Health management involves planning the optimal maintenance schedule according to the system’s current and future health condition, its critical components and the replacement costs. Construction of health indicators is the key to predicting the remaining useful life. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. Because it is difficult to measure and quantify the health conditions of bearings and gears in many cases, numerous vibration-based methods have been proposed to construct bearing and gear health indicators. This paper presents a thorough review of vibration-based bearing and gear health indicators constructed from mechanical signal processing, modeling, and machine learning. This review paper will be helpful for designing further advanced bearing and gear health indicators and provides a basis for predicting the remaining useful life of bearings and gears. Most of the bearing and gear health indicators reviewed in this paper are highly relevant to simulated and experimental run-to-failure data rather than artificially seeded bearing and gear fault data. Finally, some problems in the literature are highlighted and areas for future study are identified.

326 citations

Journal ArticleDOI
TL;DR: In this article, the authors provide a review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis.
Abstract: Since the early 1980s, wind power technology has experienced an immense growth with respect to both the turbine size and market share. As the demand for large-scale wind turbines and lor operation & maintenance cost continues to raise, the interest on condition monitoring system has increased rapidly. The main components of wind turbines are the focus of all CMS since they frequently cause high repair costs and equipment downtime. However, vast quantities of their failures are caused due to a bearing failure. Therefore, bearing condition monitoring becomes crucial. This paper aims at providing a state-of-the-art review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, wear debris analysis and vibration analysis. Furthermore, this paper will present a literature review and discuss several technical, financial and operational challenges from the purchase of the CMS to the wind farm monitoring stage.

248 citations

Journal ArticleDOI
TL;DR: In this article, an ensemble empirical mode decomposition (EEMD) and Jensen Renyi divergence (JRD) based methodology is proposed for the degradation assessment of rolling element bearings using vibration data.

98 citations

Journal ArticleDOI
TL;DR: A novel fault diagnosis method using multivibration signals and deep belief network (DBN) can adaptively fuse multifeature data and identify various bearing faults and obtain higher identification accuracy than other methods.
Abstract: In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN). By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.

91 citations