# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network  
[PERSON]  
1 Graduate Institute of Precision Manufacturing, National Chin-Y1 University of Technology,  
Taichung 41130, Taiwan; [EMAIL_ADDRESS] (W-C.S. ); [EMAIL_ADDRESS] (F.F)  
1  
[PERSON]  
1 Graduate Institute of Precision Manufacturing, National Chin-Y1 University of Technology,  
Taichung 41130, Taiwan; [EMAIL_ADDRESS] (W-C.S. ); [EMAIL_ADDRESS] (F.F)  
1  
[PERSON]  
2  
[PERSON]  
Correspondence: [EMAIL_ADDRESS]; Tel. : +886-4-2392-4505 (ext. 7181); Fax: +886-4-2392-5714 21339  
that the preload loss rate is influenced by the external loading, running speed, and surface asperities. In addition, the wear of the linear guide was shown to affect certain vibration behaviors, such as resonant frequency and amplitude to different trends, depending on the wear progress with running distance [12]. Essentially, worn out contact surfaces will lessen the interference between the balls and the groove of the linear guide, and hence, reduce the preload from an initially higher amount to a lower amount. This causes the dynamic performance of the linear feeding system to deviate from the initial states [12; 13; 14]. Therefore, condition monitoring of the abnormality of a machine-based vibration analysis is an important task for preventing component failure or performance degradation of the feeding system. Consequently, a data-driven methodology for prognostics and health management (PHM) has been widely employed in condition monitoring of the abnormality of a machine for maintaining the system performance during operation [15; 16; 17]. Vibration monitoring is an important tool for the fault diagnosis of a machine condition because it can be detected by accelerometers attached to the machine and directly compared with the values assessed under health conditions. Feature extraction from vibration signals is a prerequisite for the diagnosis of the failure or fault modes of rolling components. The main feature extraction methods include wavelet transformation, empirical mode decomposition (EMD), and Hilbert-Huang transformation (HHT) [18; 19; 20; 21]. Basically, wavelet transformation is used to decompose the signal features in a high frequency region, which can help in differentiating the high frequency features from the defect-induced transient vibration of a rolling bearing. In the study by [PERSON] et al. [19], wavelet transformation was used as a preprocessor of vibration signals to improve the performance of the Hilbert-Huang transform in detecting bearing defect-induced impact vibrations. Ensemble empirical mode decomposition (EEMD) was proven to have a better capability in decomposing the vibration signals of rotating machinery with defects and extracting features with better discriminability [18; 20]. These resulting features are demonstrated as effective input vectors to the neural network to identify the fault situations of rotating machinery [18; 20; 21]. In addition, artificial intelligence algorithms, such as genetic algorithms, fuzzy theorems, and artificial neural networks (ANNs) are employed to develop a predictive model for the fault detection of rotary mechanisms with bearing components [22; 23; 24; 25]. [PERSON] et al. [23] identified the fault conditions of rolling bearings based on the vibration features by using artificial neural networks with different architectures. It is shown that the multilayer perception (MLP) integrated with the genetic algorithm (GA) has a superior fault detection performance. Essentially, for defective bearings with localized damage in the inner or outer raceway and rolling elements, the featured frequency can be characterized from the measured vibration signals based on the bearing specifications and operating speed [26]. However, ambiguous features of abnormal vibration could be induced from rolling elements with multi-faults. To enhance prediction performance, [PERSON] et al.