Application of Torque Signal Analysis of Servomotors to Assess of Support System Condition of Industrial Machining Centre

Augustyn, Damian; Fidali, Marek

doi:10.1007/978-3-031-31719-4_20

Cited by 1 publication

(1 citation statement)

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“…There are several ways to detect anomalies in torque signals, such as model-based and residue analysis or feature extraction and neural models [32]. The second approach was tested during preliminary research and the results were very promising [33]. As part of the research continuation, it was decided to verify the potential of another type of feedforward artificial neural network called an autoencoder.…”

Section: Anomaly Detection and Status Indicator Calculationmentioning

confidence: 99%

Method of Machining Centre Sliding System Fault Detection using Torque Signals and Autoencoder

Augustyn,

Fidali

2023

Acta Mechanica Et Automatica

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The sliding system of machining centres often causes maintenance and process problems. Improper operation of the sliding system can result from wear of mechanical parts and drives faults. To detect the faulty operation of the sliding system, measurements of the torque of its servomotors can be used. Servomotor controllers can measure motor current, which can be used to calculate motor torque. For research purposes, the authors used a set of torque signals from the machining centre servomotors that were acquired over a long period. The signals were collected during a diagnostic test programmed in the machining centre controller and performed once per day. In this article, a method for detecting anomalies in torque signals was presented for the condition assessment of the machining centre sliding systems. During the research, an autoencoder was used to detect the anomaly, and the condition was assessed based on the value of the reconstruction error. The results indicate that the anomaly detection method using an autoencoder is an effective solution for detecting damage to the sliding system and can be easily used in a condition monitoring system.

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Section: Anomaly Detection and Status Indicator Calculationmentioning

confidence: 99%