This paper focuses on the diagnostics of ball bearings in direct-drive motors. These specific AC brushless motors are increasing their importance in automation machineries because they can work with a built-in flexibility. In particular the angular displacement of the shaft is continuously monitored by an embedded encoder while the control system allows to perform complex motion profiles such as polynomial ones, even with the inversion of the rotating direction. Direct-drive motors avoid the presence of a mechanical cams or gearboxes between the motor and the load with a subsequent money-saving. On the other side, unfortunately, the diagnostics of ball bearing in those motors is not trivial. In fact most of the solutions proposed in the literature require a constant frequency rotation of the shaft since the characteristic fault frequencies are directly proportional to speed of the motor. It follows that in a varying speed application the fault characteristic frequencies change instantaneously as the rotational frequency does. Moreover the direct link between the motor and load introduces dynamical effects on the vibration signal of the bearing by means of the load variations. In this paper an industrial application is considered, where the direct drive motors are used in the kinematic chain of an automated packaging machine performing a cyclic polynomial profile. The basic idea is to focus on signal segmentation using the position profile of the shaft – directly measured by the encoder – as trigger. Next the single cycles of the machine is analysed in time domain, again using encoder signal machine contribution is deleted. Feature extraction for damage detection is done by applying the Short Time Fourier Transform (STFT), the STFT for each cycle is averaged in time-frequency domain in order to enhance fault signature. For Averaged Cyclic STFT , the Spectral Kurtosis is used to select the optimal frequency band. To support this decision an Energy Distribution in frequency domain is used. Finally, the sum of STFT coefficients is used as a simple indicators of damage. Detection of the status of the bearing can be done automatically

STFT based Spectral Kurtosis and Energy Distribution approach for ball bearing fault detection in a varying speed motor / Cocconcelli, Marco; R., Zimroz; Rubini, Riccardo; W., Bartelmus. - ELETTRONICO. - (2011), pp. 1-13. (Intervento presentato al convegno The International Conference Surveillance 6 tenutosi a Compiegne, Francia nel 25-26 October 2011).

### STFT based Spectral Kurtosis and Energy Distribution approach for ball bearing fault detection in a varying speed motor

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*COCCONCELLI, Marco;RUBINI, Riccardo;*

##### 2011

#### Abstract

This paper focuses on the diagnostics of ball bearings in direct-drive motors. These specific AC brushless motors are increasing their importance in automation machineries because they can work with a built-in flexibility. In particular the angular displacement of the shaft is continuously monitored by an embedded encoder while the control system allows to perform complex motion profiles such as polynomial ones, even with the inversion of the rotating direction. Direct-drive motors avoid the presence of a mechanical cams or gearboxes between the motor and the load with a subsequent money-saving. On the other side, unfortunately, the diagnostics of ball bearing in those motors is not trivial. In fact most of the solutions proposed in the literature require a constant frequency rotation of the shaft since the characteristic fault frequencies are directly proportional to speed of the motor. It follows that in a varying speed application the fault characteristic frequencies change instantaneously as the rotational frequency does. Moreover the direct link between the motor and load introduces dynamical effects on the vibration signal of the bearing by means of the load variations. In this paper an industrial application is considered, where the direct drive motors are used in the kinematic chain of an automated packaging machine performing a cyclic polynomial profile. The basic idea is to focus on signal segmentation using the position profile of the shaft – directly measured by the encoder – as trigger. Next the single cycles of the machine is analysed in time domain, again using encoder signal machine contribution is deleted. Feature extraction for damage detection is done by applying the Short Time Fourier Transform (STFT), the STFT for each cycle is averaged in time-frequency domain in order to enhance fault signature. For Averaged Cyclic STFT , the Spectral Kurtosis is used to select the optimal frequency band. To support this decision an Energy Distribution in frequency domain is used. Finally, the sum of STFT coefficients is used as a simple indicators of damage. Detection of the status of the bearing can be done automatically##### Pubblicazioni consigliate

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