Abstract
Industrial fans play a critical role in manufacturing facilities, and a sudden shutdown of critical fans can cause significant disruptions. Ensuring early, effective, and accurate detection of fan malfunctions first requires confirming the characteristics of anomalies resulting from initial damage to rotating machinery. In addition, sensing and detection must rely on the use of sensors and sensing characteristics appropriate to various operational abnormalities. This research proposes an online industrial fan monitoring and fault detection technique based on acoustic signals as a physical sensing index. The proposed system detects and assesses anomalies resulting from preliminary damage to rotating machinery, along with improved sensing resolution bandwidth features for microphone sensors as compared to accelerometer sensors. The resulting Intelligent Prediction Integration System with Internet (IPII) is built to analyze rotation performance and predict malfunctions in industrial fans. The system uses an NI cRIO-9065 embedded controller and a real-time signal sensing module. The kernel algorithm is based on an acoustic signal enhancement filter (ASEF) as well as an adaptive Kalman filter (AKF). The proposed scheme uses acoustic signals with adaptive order-tracking technology to perform algorithm analysis and anomaly detection. Experimental results showed that the acoustic signal and adaptive order analysis method could effectively perform real-time early fault detection and prediction in industrial fans.
Highlights
Industrial fans are critical components in industrial facilities and are used to remove exhaust emissions, ventilate, compress air, and drive air-conditioning systems
Our acoustic signal enhancement filter (ASEF) features acoustic-sensor-based nonperiodic acoustic signal processing for an industrial fan. It begins with the Karhunen-Loeve Transform (KLT) filtering, followed by the melscale frequency cepstral coefficient (MFCC) filtering
The monitoring system uses a high-resolution order-tracking analysis, combining the big data collected from a microphone, accelerometer, and tachometer with the bearing temperature signals to execute the algorithm to implement fault prediction for industrial fans based on acoustic signals
Summary
Industrial fans are critical components in industrial facilities and are used to remove exhaust emissions, ventilate, compress air, and drive air-conditioning systems. An acoustic sensor possesses superior highfrequency range sensing characteristics to match the signal characteristics of early failure in industrial fans and process fault detection through the order-tracking analysis and adaptive Kalman filter algorithms with good detection results. We use high-resolution-order-analysis technology to analyze the characteristics of acoustic and vibration signals in terms of rotation speed to capture information related to the operation of rotating equipment This information is combined with the adaptive Kalman filter algorithm to produce an early fault detection method for rotating equipment. For the nonperiodic dynamic signal, it relies mainly on the ASEF algorithm along with the acoustic signal to perform abnormal feature analysis for early diagnosis of the failure It is an innovative method for predictive maintenance in factories. Adjusting or modifying the parameters during production processes can prevent production shutdowns and reduce maintenance costs
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