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您的检索式：作者名="Andrew D.Ball"
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| 1 | Implementation of Envelope Analysis on a Wireless Condition Monitoring System for Bearing Fault Diagnosis显示文摘Envelope analysis is an effective method for characterizing impulsive vibrations in wired condition monitoring(CM)systems. This paper depicts the implementation of envelope analysis on a wireless sensor node for obtaining a more convenient and reliable CM system. To maintain CM performances under the constraints of resources available in the cost effective Zigbee based wireless sensor network(WSN), a low cost cortex-M4 F microcontroller is employed as the core processor to implement the envelope analysis algorithm on the sensor node. The on-chip 12 bit analog-to-digital converter(ADC) working at 10 k Hz sampling rate is adopted to acquire vibration signals measured by a wide frequency band piezoelectric accelerometer. The data processing flow inside the processor is optimized to satisfy the large memory usage in implementing fast Fourier transform(FFT) and Hilbert transform(HT). Thus, the envelope spectrum can be computed from a data frame of 2048 points to achieve a frequency resolution acceptable for identifying the characteristic frequencies of different bearing faults. Experimental evaluation results show that the embedded envelope analysis algorithm can successfully diagnose the simulated bearing faults and the data transmission throughput can be reduced by at least 95% per frame compared with that of the raw data, allowing a large number of sensor nodes to be deployed in the network for real time monitoring. | Guo-Jin Feng James Gu Dong Zhen Mustafa Aliwan Feng-Shou Gu Andrew D.Ball | 2015 | International Journal of Automation and computing2015，12，1： | 28 |
| 2 | Orthogonal On-Rotor Sensing Vibrations for Condition Monitoring of Rotating Machines显示文摘Thanks to the fast development of micro-electro-mechanical systems(MEMS)technologies,MEMS accelerometers show great potentialities for machine condition monitoring.To overcome the problems of a poor signal to noise ratio(SNR),complicated modulation,and high costs of vibration measurement and computation using conventional integrated electronics piezoelectric accelerometers,a triaxialMEMS accelerometer-based on-rotor sensing(ORS)technology was developed in this study.With wireless data transmission capability,the ORS unit can be mounted on a rotating rotor to obtain both rotational and transverse dynamics of the rotor with a high SNR.The orthogonal outputs lead to a construction method of analytic signals in the time domain,which is versatile in fault detection and diagnosis of rotating machines.Two case studies based on an induction motor were carried out,which demonstrated that incipient bearing defect and half-broken rotor bar can be effectively diagnosed by the proposed measurement and analysis methods.Comparatively,vibration signals from translational on-casing accelerometers are less capable of detecting such faults.This demonstrates the superiority of the ORS vibrations in fault detection of rotating machines. | Yuandong Xu Xiaoli Tang Guojin Feng Dong Wang Craig Ashworth Fengshou Gu Andrew D.Ball | 2022 | Journal of Dynamics, Monitoring and Diagnostics2022，1，1： | 0 |
| 3 | An Approach to Reducing Input Parameter Volume for Fault Classifiers显示文摘As condition monitoring of systems continues to grow in both complexity and application, an overabundance of data is amassed. Computational capabilities are unable to keep abreast of the subsequent processing requirements. Thus, a means of establishing computable prognostic models to accurately reflect process condition, whilst alleviating computational burdens, is essential. This is achievable by restricting the amount of information input that is redundant to modelling algorithms. In this paper, a variable clustering approach is investigated to reorganise the harmonics of common diagnostic features in rotating machinery into a smaller number of heterogeneous groups that reflect conditions of the machine with minimal information redundancy. Na?ve Bayes classifiers established using a reduced number of highly sensitive input parameters realised superior classification powers over higher dimensional classifiers,demonstrating the effectiveness of the proposed approach. Furthermore, generic parameter capabilities were evidenced through confirmatory factor analysis. Parameters with superior deterministic power were identified alongside complimentary, uncorrelated, variables.Particularly, variables with little explanatory capacity could be eliminated and lead to further variable reductions. Their information sustainability is also evaluated with Na?ve Bayes classifiers, showing that successive classification rates are sufficiently high when the first few harmonics are used. Further gains were illustrated on compression of chosen envelope harmonic features. A Na?ve Bayes classification model incorporating just two compressed input variables realised an 83.3% success rate, both an increase in classification rate and an immense improvement volume-wise on the former ten parameter model. | Ann Smith Fengshou Gu Andrew D.Ball | 2019 | International Journal of Automation and computing2019，16，2： | 0 |
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