Abstract
Purpose: Condition-based maintenance requires an accurate detection of unknown yet-to-have-occurred anomalies and the establishment of anomaly detection procedure for sensor data is urgently needed. Sensor data are noisy, and a conventional analysis cannot always be conducted appropriately. An anomaly detection procedure for noisy data was therefore developed.Methodology/Approach: In a conventional Mahalanobis–Taguchi method, appropriate anomaly detection is difficult with noisy data. Herein, the following is applied: 1) estimation of a statistical model considering noise, 2) its application to anomaly detection, and 3) development of a corresponding analysis framework.Findings: Engineers can conduct anomaly detection through the measurement and accumulation, analysis, and feedback of data. Especially, the two-step estimation of the statistical model in the analysis stage helps because it bridges technical knowledge and advanced anomaly detection.Research Limitation/implication: A novel data-utilisation design regarding the acquired quality is provided. Sensor-collected big data are generally noisy. By contrast, data targeted through conventional statistical quality control are small but the noise is controlled. Thus various findings for quality acquisition can be obtained. A framework for data analysis using big and small data is provided.Originality/Value of paper: The proposed statistical anomaly detection procedure for noisy data will improve of the feasibility of new services such as condition-based maintenance of equipment using sensor data.
Highlights
1 INTRODUCTION In recent years, the condition-based maintenance of equipment using sensor data has been put into practical use in the Japanese manufacturing industry based on the progress of technologies related to the Internet of Things (IoT)
The anomaly detection procedure used in the MT method, which is a representative methodology based on the Taguchi method, has been improved such that noisy data can be properly analysed
The proposal covers all three stages of data utilisation, a two-step estimation of the statistical model in the analysis stage is useful in the sense that it fills in the gap between the engineer’s technical knowledge and advanced anomaly detection
Summary
The condition-based maintenance of equipment using sensor data has been put into practical use in the Japanese manufacturing industry based on the progress of technologies related to the Internet of Things (IoT). Such noise is additively superimposed over the true value of the measurement, and y = x + e is established As described, this model is related to the engineered system used in the Taguchi method. This model is related to the engineered system used in the Taguchi method When dealing with such noisy data, problems can arise in which it becomes difficult for engineers to consider adopting anomaly detection algorithms from the viewpoint of intrinsic technology. Taking the example of the formation process of a porous film, it can be stated that it is easy for skilled engineers to consider the relation between the physical properties of the porous film from the true measurement value. There is a risk that the adopted anomaly detection algorithms will lack validity from the viewpoint of intrinsic technology
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