Machine learning for anomaly detection and process phase classification to improve safety and maintenance activities

• Published in: Journal of manufacturing systems Vol. 56; pp. 117 - 132
• Main Authors: Quatrini, Elena, Costantino, Francesco, Di Gravio, Giulio, Patriarca, Riccardo
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.07.2020
• Subjects: Condition monitoring; Condition-based maintenance; Decision jungles; forests; Machine learning; Random; Condition monitoring; Random; forests; Condition-based maintenance; Decision jungles; Machine learning
• DOI: 10.1016/j.jmsy.2020.05.013
• ISSN: 0278-6125

•Define a methodology for anomaly detection with real time data from multiphase industrial process.•RFA and DJA have comparable results for the identification of process phases.•RFA has better performance than DJA for the anomaly detection.•DJA underperforms for anomalies close to the thresholds, due to its increase of generalization. Anomaly detection is a crucial aspect for both safety and efficiency of modern process industries. This paper proposes a two-steps methodology for anomaly detection in industrial processes, adopting machine learning classification algorithms. Starting from a real-time collection of process data, the first step identifies the ongoing process phase, the second step classifies the input data as “Expected”, “Warning”, or “Critical”. The proposed methodology is extremely relevant where machines carry out several operations without the evidence of production phases. In this context, the difficulty of attributing the real-time measurements to a specific production phase affects the success of the condition monitoring. The paper proposes the comparison of the anomaly detection step with and without the process phase identification step, validating its absolute necessity. The methodology applies the decision forests algorithm, as a well-known anomaly detector from industrial data, and decision jungle algorithm, never tested before in industrial applications. A real case study in the pharmaceutical industry validates the proposed anomaly detection methodology, using a 10 months database of 16 process parameters from a granulation process.