Gear misalignment diagnosis using statistical features of vibration and airborne sound spectrums

Khan, Muhammad Ali; Shahid, Muhammad Atayyab; Ahmed, Syed Adil; Khan, Sohaib Z.; Khan, Kamran A.; Ali, Syed Asad; Tariq, Muhammad Ahsan

doi:10.1016/j.measurement.2019.05.088

Cited by 35 publications

(23 citation statements)

References 27 publications

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“…Kurtosis can be used to measure the heaviness of the tails in the distribution of the signal x [32]. The Kurtosis of a normal distribution is 3 [35]. So, kurtosis is used to describe the convexity of non-Gaussian probability density distribution.…”

Section: ) Kurtosis Featurementioning

confidence: 99%

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Zhang

Guo

et al. 2020

IEEE Access

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In the soft fault diagnosis of nonlinear analog filter circuits, the single feature can't maximally reveal the behaviors hidden in signals. In order to overcome such shortcomings, a fusion algorithm weighted feature from multi-group is proposed. This method use reliefF algorithm to optimize canonical correlation analysis combines support vector machine(RCCA-SVM) for diagnosis. The fault characteristics used in this method are extracted from the time-domain, statistical features and frequency-domain by wavelet packet transform (WPT). And then the CCA algorithm is used to improve the correlation of features according to the weights of the features. Finally, the fusion features are dimension reduced by principal component analysis(PCA), support vector machine(SVM) is the classifier of the diagnosis. The simulations show that the proposed method has a good diagnostic effect on circuit fault diagnosis of non-linear analog circuits. INDEX TERMS Nonlinear analog circuit fault diagnosis, WPT, RCCA-SVM.

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Section: ) Kurtosis Featurementioning

confidence: 99%

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Zhang

Guo

et al. 2020

IEEE Access

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“…If such devices fail, the results can often be catastrophic accidents with serious consequences. Therefore, a proactive approach must be adopted that enables such components to be monitored in real-time utilizing predictive maintenance methods (Khan et al, 2019). In the present study, the technique of vibration analysis is approached.…”

Section: Implementation On a Case Studymentioning

confidence: 99%

“…Function, failure mode, failure cause, and failure effect for test rig. Types of shaft misalignment adapted fromKhan et al (2019).…”

mentioning

confidence: 99%

Ontology-Based Context Modeling in Physical Asset Integrity Management

Al-Shdifat

Emmanouilidis

Khan

et al. 2020

Front. Comput. Sci.

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Asset management is concerned with the management practices, technologies and tools necessary to maximize the value delivered by physical engineering assets. IoT-generated data are increasingly considered as an asset and the data asset value needs to be maximized too. However, asset-generated data in practice are often collected in non-actionable form. Collected data may comprise a wide number of parameters, over long periods of time and be of significant scale. Yet they may fail to represent the range of possible scenarios of asset operation or the causal relationships between the monitored parameters, and so the size of the data collection, while adding to the complexity of the problem, does not necessarily allow direct data asset value exploitation. One way to handle data complexity is to introduce context information modeling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. The aim of the present paper is, therefore, 2-fold: to analyse current approaches to addressing IoT context information management, mapping how context-aware computing addresses key challenges and supports the delivery of monitoring solutions; and to develop a maintenance context ontology focused on failure analysis of mechanical components so as to drive monitoring services adaptation. The approach is demonstrated by applying the ontology on an industrially relevant physical gearbox test rig, designed to model complex misalignment cases met in manufacturing and aerospace applications.

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“…Vibration analysis is the most common technique for monitoring and diagnosing mechanical systems [23][24][25]. e signal acquisition for certain mechanical faults is accomplished by installing an accelerometer or proximity probe [26,27]. However, due to the confusing spectral characteristics of vibration, vibrationbased misalignment detection led to less reliability [10].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis of the Misaligned Faults of the Vertical Test Instrument of High‐Precision Industrial Robot Reducer

Zhang

2021

Shock and Vibration

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High-precision reducer is the core component of industrial robots. In order to achieve the comprehensive performance testing of precision reducers, an instrument with a vertical layout and a cylindrical structure is designed. As a rotating machine, the inevitable coupling misalignment of the instrument can lead to vibration faults which lead to errors in the test. So it is pretty necessary to diagnose and monitor the coupling misalignment while the instrument is working. The causes of the coupling misaligned fault of the instrument and the relationship between the misalignment fault and torque ripple are analyzed in this paper. A method of using the torque transducer in the measurement chain of the instrument to diagnose the coupling misalignment is proposed in this paper. Many experiments have been done to test the capability of detecting the coupling misalignment using this method. Experimental results show that the amplitude of torque ripple of the shaft is linearly related to the coupling misalignment and is quadratically related to the rotation speed of the shaft when the misalignment exists in the shaft. The combination of components at the rotation frequency (fr) and the additional components can be used to diagnose faults due to coupling misalignment.

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Gear misalignment diagnosis using statistical features of vibration and airborne sound spectrums

Cited by 35 publications

References 27 publications

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Ontology-Based Context Modeling in Physical Asset Integrity Management

Diagnosis of the Misaligned Faults of the Vertical Test Instrument of High‐Precision Industrial Robot Reducer

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