A novel unsupervised method for anomaly detection in time series based on statistical features for industrial predictive maintenance

Industrial production processes are increasingly collecting data from machines in operation due to the cost reduction and popularization of sensor technologies. Valuable information is generated by using the right sensors and proper techniques on the machine’s current state in operation. This extraction allows detecting whether the machine is operating in a degraded state and then, if necessary, interrupts its operation before it goes into a broken state. The detection of the abnormal behavior of a machine is relevant since detection at the right time can reduce financial costs due to machine breakdown and production downtime. This work proposes a novel unsupervised method to detect anomalies in industrial machines and interrupt their operation before this machine goes into a state of breakdown. The proposed method receives as input a set of time series data from several sensors. Using a collection of statistical features, it calculates an indicator that characterizes the failure’s severity, issuing an alert to the machine operator if necessary. The method was evaluated in two benchmarks with known univariate data and two proprietary datasets with multivariate data. Conducted experiments revealed the low computational time spent on training and on evaluation. Overall results measured in Area Under the Receiver Operating Characteristic Curve (AUC) in Yahoo’s benchmark were 89.3%; in Numenta, it was 70.3%, and in the two multivariate datasets evaluated, it was 92.4% and 91.2%. These high AUC values reveal the potential of the proposed method applied in predictive maintenance in a large soybean oil production industry.