Time-Frequency Analysis of Accelerometery Data for Detection and Identification of Faults on a Pneumatic Production Machine

článek ve sborníku ve WoS nebo Scopus

en

As pneumatic cylinders are nowadays an integral part of manufacturing machines in a variety of industries, there is a growing demand for development of cost-effective and reliable techniques for detection and identification of faults on these machines. In this paper, we propose a novel algorithm for condition indicator extraction from the time-frequency domain. The algorithm uses signal-based condition indicators extracted from a frequency spectrum obtained by a modified version of the Short-Time Fourier Transform (STFT). The aim of this article is to demonstrate that the processing of readings from an accelerometer by the proposed method provides higher accuracy in detecting and identifying faults. We achieve all this by processing only a fraction of the measured signal. As result of that, the computational requirements are decreasing, leading to lower hardware cost which is advantageous as the proposed method is developed for industrial deployment.

As pneumatic cylinders are nowadays an integral part of manufacturing machines in a variety of industries, there is a growing demand for development of cost-effective and reliable techniques for detection and identification of faults on these machines. In this paper, we propose a novel algorithm for condition indicator extraction from the time-frequency domain. The algorithm uses signal-based condition indicators extracted from a frequency spectrum obtained by a modified version of the Short-Time Fourier Transform (STFT). The aim of this article is to demonstrate that the processing of readings from an accelerometer by the proposed method provides higher accuracy in detecting and identifying faults. We achieve all this by processing only a fraction of the measured signal. As result of that, the computational requirements are decreasing, leading to lower hardware cost which is advantageous as the proposed method is developed for industrial deployment.

fault diagnosis; pneumatic system; Short-Time Fourier Transform; signal-based features

07.12.2022

Institute of Electrical and Electronics Engineers

Plzeň

978-1-6654-1040-3

Proceedings of the 2022 20th International Conference on Mechatronics - Mechatronika (ME)

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