Fault detection method for one-class support vector machine based on density parameter optimization

Abstract

The invention relates to a fault detection method for a one-class support vector machine based on density parameter optimization. The fault detection method comprises the following steps: 1) arranging a fault detection device which comprises data acquisition equipment, a confidence level arranging module and a supervisory control computer; 2) acquiring normal data generated during a production process by the data acquisition equipment, inputting the normal data into the supervisory control computer, and performing normalization treatment, thereby acquiring a total sample set Q; 3) setting a confidence level value alpha; 4) inputting the total sample set Q and the confidence level value alpha into a generating module of an optimized fault detector, and optimizing and acquiring a model of the optimized fault detector; 5) storing the model of the optimized fault detector by an application module of the optimized fault detector; and 6) in an industrial production process, inputting the acquired data into the supervisory control computer by the data acquisition equipment, after performing the normalization treatment on the data, inputting the data into the application module of the optimized fault detector, and outputting a fault detection result in real time by the optimized fault detector stored in the application module of the optimized fault detector. The fault detection methodprovided by the invention can be widely applied to the fault detection for the running state of an industrial production line.

Description

technical field [0001] The invention relates to a data-driven production process fault detection method, in particular to a density-based parameter optimization single classification support vector machine (One-class Support Vector Machine, OCSVM) fault detection method. Background technique [0002] The automation process of industrial production requires real-time detection of the equipment operating status of the industrial production process through various sensors, and analyzes whether the production process is in an abnormal operating state based on the detected data. Usually, this kind of sensor detection data is used to analyze the abnormality of the production process. The method is called based on data-driven fault detection method in production process. When the detection data has a strong linear relationship and satisfies the Gaussian distribution, PCA (Principal Component Analysis, principal component analysis) can be used to assist SPE (Squared Prediction Error...

AI Technical Summary

Problems solved by technology

[0006] However, when using AUC as an evaluation index, a certain number of abnormal samples are required to draw the ROC curve and the corresponding AUC value with practical use value

However, most of the data collected in the industrial production process are normal samples, so it is difficult to obtain a sufficient number of abnormal samples, or the obtained abnormal samples do not reflect all abnormal situations

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