| الملخص: | In predictive maintenance systems, the vibration sensors used during the data collection phase may be subjected to human or environmental interference, leading to data anomalies. A secure and reliable integrated pre-detection scheme is proposed to ensure the reliability of the collected data. This scheme combines random open strategies, similarity detection methods, and sound source localization technologies to enhance the accuracy and reliability of the system in terms of both spatial and temporal dimensions. First, a random open strategy is used to ensure that the sensors are not subjected to directional interference, thereby enhancing the safety redundancy of the system. Second, a similarity detection method utilizes multi-dimensional distances to calculate the similarity of consecutive acceleration data collected by vibration sensors and compares it with a threshold to increase the sensitivity of the system to equipment status. Finally, a sound source localization technology analyzes the audio corresponding to abnormal similarities to determine the source location, further enhancing the precision of pre-detection. Experimental results in targeted testing environments indicate that in non-adversarial scenarios, the non-integrated scheme improves the accuracy and precision by 4 and 4.13 percentage points, respectively, compared to the integrated scheme and the recall remains the same. Conversely, in adversarial scenarios, the integrated scheme improves the accuracy and recall by 9.5 and 9.14 percentage points, respectively, compared to the non-integrated scheme, with the precision remaining constant.
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