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Intelligent Anomaly Detection and Real Time Monitoring of Press Machine
With the advent of the Industry 4.0 era, many smart manufacturing related technologies have been used in various industries. Among them, the industry that has a great impact on Taiwan’s economy is the manufacturing industry, which is gradually moving from traditional to modern processes. This shift results in all kinds of machinery becoming the most crucial, core equipment in the manufacturing industry. If the parts of the machinery are abnormal or overused, accidents may even happen in the workplace and the productivity was decreased. It often results huge losses for the company. In order to avoid such situations, the company usually maintains its machinery on a regular basis, or inspected by experienced technicians. The aforementioned method is cost intensive, in addition, some unexpected fault of machinery often occurs. In regards to this, an intelligent anomaly detection and real time monitoring system is proposed to solve the aforementioned drawbacks. The proposed system consists of two parts: a real time monitoring system and an intelligent anomaly detection system. The real time monitoring system deals with the signals with low sampling rate such as current, voltage, temperature and pressure. These signals are collected by the corresponding sensors and transmitted to server via programming logic device. The employees will receive an alarm message when the values of signal excess some predefined values. The intelligent anomaly detection system deals with the signals with high sampling rate such as vibration signals. The vibration signals are collected by accelerometers and transmitted to server via data acquisition device. The convolutional neural network is used as a classifier to detect the anomaly of the belt of the press machine. The experimental results show the proposed intelligent anomaly detection system has a high accuracy (98%) to distinguish the different anomalies of belt. We hope that this study can improve the added value of press machine and contribute to the field of the smart manufacturing.
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