Other affiliations: Indian Institute of Technology Delhi
Bio: Piyush Shakya is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topic(s): Bearing (mechanical) & Hilbert–Huang transform. The author has an hindex of 6, co-authored 12 publication(s) receiving 134 citation(s). Previous affiliations of Piyush Shakya include Indian Institute of Technology Delhi.
TL;DR: In this paper, a methodology is developed for defect type identification in rolling element bearings using the integrated Mahalanobis-Taguchi-Gram-Schmidt (MTGS) method.
Abstract: A methodology is developed for defect type identification in rolling element bearings using the integrated Mahalanobis–Taguchi–Gram–Schmidt (MTGS) method. Vibration data recorded from bearings with seeded defects on outer race, inner race and balls are processed in time, frequency, and time–frequency domains. Eleven damage identification parameters (RMS, Peak, Crest Factor, and Kurtosis in time domain, amplitude of outer race, inner race, and ball defect frequencies in FFT spectrum and HFRT spectrum in frequency domain and peak of HHT spectrum in time–frequency domain) are computed. Using MTGS, these damage identification parameters (DIPs) are fused into a single DIP, Mahalanobis distance (MD), and gain values for the presence of all DIPs are calculated. The gain value is used to identify the usefulness of DIP and the DIPs with positive gain are again fused into MD by using Gram–Schmidt Orthogonalization process (GSP) in order to calculate Gram–Schmidt Vectors (GSVs). Among the remaining DIPs, sign of GSVs of frequency domain DIPs is checked to classify the probable defect. The approach uses MTGS method for combining the damage parameters and in conjunction with the GSV classifies the defect. A Defect Occurrence Index (DOI) is proposed to rank the probability of existence of a type of bearing damage (ball defect/inner race defect/outer race defect/other anomalies). The methodology is successfully validated on vibration data from a different machine, bearing type and shape/configuration of the defect. The proposed methodology is also applied on the vibration data acquired from the accelerated life test on the bearings, which established the applicability of the method on naturally induced and naturally progressed defect. It is observed that the methodology successfully identifies the correct type of bearing defect. The proposed methodology is also useful in identifying the time of initiation of a defect and has potential for implementation in a real time environment.
TL;DR: In this paper, a methodology for the online detection of health status of rolling element bearing into various damage stages for naturally progressing defect is proposed for online monitoring and damage stage detection, which is successfully verified on the vibration data acquired from the naturally induced and progressed defect experiments.
Abstract: A methodology is proposed for the online detection of health status of rolling element bearing into various damage stages for naturally progressing defect. Various damage identification parameters are derived from processing vibration data in time domain, frequency domain and time–frequency domain. The parameters are fused into a single parameter, Mahalanobis distance, by application of Gram–Schmidt Orthogonalization process. Chebyshev׳s inequality is applied to the Mahalanobis distance for online monitoring and damage stage detection. A simulation study is first carried out to show working of the proposed methodology in presence of varying trends of damage identification parameters. The proposed methodology is then validated on experimental data. The first validation is on the vibration data acquired from a bearing having seeded defect. Later, two accelerated life tests are conducted on a specially designed test rig at different load and speed combinations on the bearings for ensuring naturally induced and progressed defects. The methodology is successfully verified on the vibration data acquired from the naturally induced and progressed defect experiments.
01 Oct 2013
TL;DR: Shakya et al. as discussed by the authors performed a comparative study of various vibration signal-based damage identification parameters for rolling element bearings and concluded that the results suggest that the ranking is quite consistent, even with a different bearing type and damage characteristic.
Abstract: Piyush Shakya, Ashish K Darpe and Makarand S Kulkarni are with the Vibration Research Laboratory, Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi – 110016, India. A comparative study of various vibration signal-based damage identification parameters for rolling element bearings is undertaken. Defects of varying severity are seeded on the outer and inner races of a double-row angular contact bearing. The influence of a defect and its severity on the observed identification parameters is investigated using vibration data acquired from the bearing housing. A comparison among the various time domain, frequency domain and time-frequency domain parameters is made based on their robustness, sensitivity to damage change and early detectivity of the bearing faults. An overall ranking of the parameters is attempted with the objective of ascertaining effective damage identification parameters from among those available for diagnosis of the rolling element bearings. Validation of the ranking is carried out with the data obtained on a different test-rig for early detectivity of damage. The results suggest that the ranking is quite consistent, even with a different bearing type and damage characteristic.
TL;DR: In this article, a methodology is proposed for classification of type of defect in a bearing using vibration data using Hilbert-Huang Transform (HHT) is used to obtain Instantaneous energy density (IE) values correspond to the vibration data.
Abstract: A methodology is proposed for classification of type of defect in a bearing using vibration data. Hilbert-Huang Transform (HHT) is used to obtain Instantaneous energy density (IE) values correspond...
01 Feb 2016-Measurement
TL;DR: In this paper, the authors explored the bearing diagnosis capabilities of proximity probes by exploiting its advantages and alleviating its shortcomings using appropriate signal processing of the raw time domain data and proposed a Time Synchronous Averaging based method.
Abstract: Employing multiple sensors that generate different physical parameters from the measured system to monitor its health increases the diagnosis reliability. In the present work, bearing diagnosis capabilities of proximity probes are explored by exploiting its advantages and alleviating its shortcomings using appropriate signal processing of the raw time domain data. A Time Synchronous Averaging based method is proposed for processing of the data acquired by proximity probes and its benefit is illustrated on test bearings. Simultaneous synchronous data is acquired with the help of proximity probes and accelerometer during a life test as the defect is naturally induced and progressed with time. The proximity probe is shown to perform better diagnosis for inner race defect compared to accelerometer due to a direct transmission path for this defect. The use of proximity probe can effectively supplement the information from accelerometer and improve the accuracy of bearing diagnosis.
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.
Abstract: Machinery prognostics is one of the major tasks in condition based maintenance (CBM), which aims to predict the remaining useful life (RUL) of machinery based on condition information. A machinery prognostic program generally consists of four technical processes, i.e., data acquisition, health indicator (HI) construction, health stage (HS) division, and RUL prediction. Over recent years, a significant amount of research work has been undertaken in each of the four processes. And much literature has made an excellent overview on the last process, i.e., RUL prediction. However, there has not been a systematic review that covers the four technical processes comprehensively. To fill this gap, this paper provides a review on machinery prognostics following its whole program, i.e., from data acquisition to RUL prediction. First, in data acquisition, several prognostic datasets widely used in academic literature are introduced systematically. Then, commonly used HI construction approaches and metrics are discussed. After that, the HS division process is summarized by introducing its major tasks and existing approaches. Afterwards, the advancements of RUL prediction are reviewed including the popular approaches and metrics. Finally, the paper provides discussions on current situation, upcoming challenges as well as possible future trends for researchers in this field.
01 Jan 2018-IEEE Access
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.
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.
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.
Abstract: 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. The EEMD decomposes vibration signals into a set of intrinsic mode functions (IMFs). A systematic methodology to select IMFs that are sensitive and closely related to the fault is proposed in the paper. The change in probability distribution of the energies of the sensitive IMFs is measured through JRD which acts as a damage identification parameter. Evaluation of JRD with sensitive IMFs makes it largely unaffected by change/fluctuations in operating conditions. Further, an algorithm based on Chebyshev's inequality is applied to JRD to identify exact points of change in bearing health and remove outliers. The identified change points are investigated for fault classification as possible locations where specific defect initiation could have taken place. For fault classification, two new parameters are proposed: ‘α value’ and Probable Fault Index, which together classify the fault. To standardize the degradation process, a Confidence Value parameter is proposed to quantify the bearing degradation value in a range of zero to unity. A simulation study is first carried out to demonstrate the robustness of the proposed JRD parameter under variable operating conditions of load and speed. The proposed methodology is then validated on experimental data (seeded defect data and accelerated bearing life test data). The first validation on two different vibration datasets (inner/outer) obtained from seeded defect experiments demonstrate the effectiveness of JRD parameter in detecting a change in health state as the severity of fault changes. The second validation is on two accelerated life tests. The results demonstrate the proposed approach as a potential tool for bearing performance degradation assessment.
05 Sep 2016-Shock and Vibration
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.