Edge Computing Enabled Production Anomalies Detection and Energy-Efficient Production Decision Approach for Discrete Manufacturing Workshops

Abstract

Due to the complexity and dynamics of manufacturing processes, there are various production anomalies in a discrete manufacturing workshop, which have a strong impact on manufacturing quality and productivity. Meanwhile, with the rapid development of Internet of Things technology and communication technology, data store and timely response become new challenges for production anomalies detection. Thus, an edge computing enabled production anomalies detection and energy-efficient production decision approach is proposed in this study. Firstly, an architecture of edge computing enabled production anomalies detection and energy-efficient rescheduling approach is introduced. Then, considering that the raw energy data are always large, isolated and messy, an energy consumption data preprocessing algorithm is established, and production anomaly analysis model is constructed based on long short-term memory network. When an anomaly occurs, an energy-efficient production decision making will be triggered. Finally, through a case analysis of a milling manufacturing system, the results show that the anomaly detection error of the proposed method is only 3.5%. This method realizes the combination of energy consumption data and manufacturing system anomalies detection, and can further assist production process monitoring and energy conservation. INDEX TERMS Edge computing; production anomalies detection; energy-efficient; long short-term memory network; discrete manufacturing workshops.

Artificial Intelligence Applications for Increasing Resource Efficiency in Manufacturing Companies—A Comprehensive Review

“…Zhang and Ji [99] developed an LSTM method for production error detection and energy-efficient scheduling. Thus, an increase in both energy efficiency, of 21.3%, and in product quality were achieved in a case study [99].…”

“…Zhang and Ji [99] developed an LSTM method for production error detection and energy-efficient scheduling. Thus, an increase in both energy efficiency, of 21.3%, and in product quality were achieved in a case study [99]. Reger et al [100] used pattern recognition and Markov chain to classify electric drives in manufacturing plants.…”

“…-Predictive Maintenance [57,59,69,70,80,83,84,86,95,113]; -Production planning [52,54,61,65,72,77,78,101,102]; -Fault detection and prediction/predictive quality [58,62,74,82,87,89,93,94,110,111,115,118]; -Increasing energy efficiency in production [56,63,85,99,100,103,108,114,119] and facility management [53,67,76,107,109].…”

A case study in smart manufacturing: predictive analysis of cure cycle results for a composite component

“…In terms of temperature optimization, Carlone et al [26] proposed a solution based on artificial neural networks to predict the composite temperature profile during the autoclave curing process. Other sustainable manufacturing attempts regarded energy optimization [27,28] and material efficiency [29] . The paper in [30] , similarly to the proposal, aims to reduce defects for more sustainable productivity.…”

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

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