Unsupervised Anomaly Detection of Intermittent Demand for Spare Parts Based on Dual-Tailed Probability

Abstract

The quick development of machine learning techniques provides a superior capability for manufacturing enterprises to make effective decisions about inventory management based on spare parts demand (SPD) data. Since SPD sequences in practical maintenance applications usually show an intermittent distribution, it is not easy to represent the demand pattern of such sequences. Meanwhile, there are some aspects like manual report errors, environmental interference, sudden project changes, etc., that bring large and unexpected fluctuations to SPD sequences, i.e., anomalous demands. The inventory decision made based on the SPD sequences with anomalous demands is not trusted by enterprise engineers. For such SPD data, there are two great concerns, i.e., false alarms in which sparse demands are recognized to be anomalous and missing alarms in which the anomalous demands are categorized as normal due to their adjacent demands having extreme values. To address these concerns, a new unsupervised anomaly-detection method for intermittent time series is proposed based on a dual-tailed probability. First, the multi-way delay embedding transform (MDT) was applied on the raw SPD sequences to obtain higher-order tensors. Through Tucker tensor decomposition, the disturbance of extreme demands can be effectively reduced. For the reconstructed SPD sequences, then, the tail probability at each time point, as well as the empirical cumulative distribution function were calculated based on the probability of the demand occurrence. Second, to lessen the disturbance of sparse demand, the non-zero demand sequence was distilled from the raw SPD sequence, with the tail probability at each time point being calculated. Finally, the obtained dual-tailed probabilities were fused to determine the anomalous degree of each demand. The proposed method was validated on the two actual SPD datasets, which were collected from a large engineering manufacturing enterprise and a large vehicle manufacturing enterprise in China, respectively. The results demonstrated that the proposed method can effectively lower the false alarm rate and missing alarm rate with no supervised information provided. The detection results were trustworthy enough and, more importantly, computationally inexpensive, showing significant applicability to large-scale after-sales parts management.