Abstract
Since artificial intelligence (AI) was introduced into engineering fields, it has made many breakthroughs. Machine learning (ML) algorithms have been very commonly used in structural health monitoring (SHM) systems in the last decade. In this study, a vibration-based early stage of bolt loosening detection and identification technique is proposed using ML algorithms, for a motor fastened with four bolts (M8 × 1.5) to a stationary support. First, several cases with fastened and loosened bolts were established, and the motor was operated in three different types of working condition (800 rpm, 1000 rpm, and 1200 rpm), in order to obtain enough vibration data. Second, for feature extraction of the dataset, the short-time Fourier transform (STFT) method was performed. Third, different types of classifier of ML were trained, and a new test dataset was applied to evaluate the performance of the classifiers. Finally, the classifier with the greatest accuracy was identified. The test results showed that the capability of the classifier was satisfactory for detecting bolt loosening and identifying which bolt or bolts started to lose their preload in each working condition. The identified classifier will be implemented for online monitoring of the early stage of bolt loosening of a multi-bolt structure in future works.
Highlights
A bolt joint is one of the most important methods for connecting structural components in engineering fields and can be assembled and reused
This study proposes a vibration-based method of early-stage bolt loosening detection and identification combined with an Machine learning (ML) classifier for a multi-bolt structure
When the decrement of a torque load is less than 6 N·m, ML classifiers cannot detect bolt loosening; a 6 N·m torque load decrement was considered the beginning of the early stages of bolt loosening in this investigation
Summary
A bolt joint is one of the most important methods for connecting structural components in engineering fields and can be assembled and reused. One of the main drawbacks of threaded fasteners is the loosening that occurs under shock or vibration conditions [1]. The loosening can cause a structure to become damaged seriously, so boltloosening detection and identification before failure of the structure are some of the most important issues in engineering. Detection and identification of bolt loosening can, keep a structure from experiencing accidents or failure, and reduce maintenance costs. The detection of bolt loosening in many fields of mechanical, aerospace, and civil engineering has been an important topic among many researchers in the last decade. The detection techniques for bolt loosening can be divided into three groups
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