# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
[24] proposed an intelligent diagnosis method constructed on the basis of possibility theory and a fuzzy neural network. This method was demonstrated with high accuracy to distinguish fault types sequentially based on symptom parameters. [PERSON] et al. [25] presented a fault detection and classification algorithm. Vibration signals were first filtered by an intelligent filter to remove the non-bearing fault components before being fed into a classifier. The results showed that the new algorithm using a non-bearing fault component (RNFC) filter not only yields higher accuracy in fault classification as compared to an adaptive neo-fuzzy inference system (ANFIS), but also improves the reliability in the case of a low-quality measured signal. These studies clearly indicate that the accuracy of a fault diagnosis greatly depends on feature extraction and classification methods. Similar to rolling bearings, linear guides are also susceptible to fault states or surface wear under long-term loading durations. A fault diagnosis can be conducted based on condition monitoring technology through a vibration signal analysis [27; 28; 29; 30; 31; 32]. As demonstrated in a previous study [27], the vibration characteristics of the feeding stage can be significantly related to the linear guide preload. Based on this significance, the fault diagnosis of a linear guide can be effectively conducted by monitoring the variation in the featured vibration frequencies and acoustic characteristics of the stage in motion. [PERSON] et al. [28] reported a study on the fault diagnosis of a ball screw system. In addition to health conditions, failure modes such as lubrication starvation, preload loss, ball nut wear, and re-circulation system failure were recognized based on the time and frequency domain features; these features were extracted from vibration signals on the ball screw, according to the multi-failure classification algorithms for each failure pattern. [PERSON] et al. [29] proposed a diagnostic method based on the FFT vibration spectrum to identify linear motor bearing faults. The characteristic frequency and amplitude are the indicators of the fault modes, such as damage to rolling elements, carriage race, or plugging of the recirculation channel. [PERSON] et al. [30; 31] developed an ANN classification model to analyze the cause of linear bearing faults in operational conditions, in which three different fault conditions, namely healthy bearing and ball bearing damage with and without starved lubricant, can be clearly identified based on the featured frequency by FFT spectrum data measured from tests. In a study by [PERSON] et al. [32], the energy distribution was extracted as a feature from decomposed vibration signals with the application of wavelet package decomposition. The extracted feature was verified as a good classifier in an ANN to identify the lubrication conditions of the rolling guide, with an accuracy of 95%. In conclusion, fault conditions with specific defects of the mechanism with rolling elements can be effectively classified by a classification model based on the vibration features associated with the failure pattern of components. However, it is noted that the selection of the vibration feature and its dependence on the failure pattern of components are the prerequisites to be examined for successfully establishing the diagnosis model [23; 25]. For rolling bearing, featured frequencies are closely related to the defect type or damage patterns of components, but they cannot accurately reflect the severity of the defects. In addition, vibration features assessed by a time domain analysis are sensitive to change with fault development in size and location, which affect not only the feature selection but also the classification performance in the subsequent fault diagnosis [33; 34]. Machine learning algorithms such as ANN and the support vector machine (SVM) are considered the appropriate tools for establishing the classification model [35].