Misalignment detection on linear feed axis using sensorless motor current signals

Abstract

Due to ageing populations and a shortage of skilled labour, automatic machine condition monitoring is a powerful tool to ensure smooth operation of production systems with reduced manpower. Automatic condition monitoring enables early detection of machine faults, greatly increasing uptime, reliability, and safety. However, conventional fault detection methods based on vibration require installation of additional sensors, thus bringing up implementation effort and initial costs. The linear feed axis is a machine component whose failure can bring an entire production line to a standstill. Therefore, this study presents a sensorless approach, which uses a linear axis' motor current for the detection of misalignment. Motor current time series data was encoded as images and then fed to a CNN, more precisely a revised residual neural network (ResNet). A random search hyper-parameter tuning technique was used to optimise the structure of the CNN. Then, transfer learning was used to apply the current signal features already learned to other scenarios. The results showed that both horizontal and vertical misalignments of linear feed axes can be well detected by a revised ResNet using motor current signals, with an accuracy of 99.76%. Using transfer learning, misalignments were detected with an accuracy of 92.67% -even under the in uence of external forces.

Linear Axis Guide Rail Misalignment Detection and Localization Using a Novel Signal Segmentation Analysis Technique

“…[36] Applied various combinations of machine learning models including convolutional neural network (CNN), autoencoder (AE), temporal convolutional network (TCN), and long short-term memory (LSTM). [37] Applied fast Fourier transform (FFT) alongside statistical feature analysis on the collected current data.…”

“…Component Analyzed Misalignment FF Creation Process Detection Method [36,37] Leadscrew Right and left misalignment (0.5 mm, 1 mm) was introduced at the second support block of the leadscrew.…”

“…Ref. [37] Applied fast Fourier transform (FFT) alongside statistical feature analysis on the collected current data. [27] Linear guide rail Vertical misalignment was introduced by removing bolts and applying a specialized pushing mechanism underneath the guide rail to cause maximum deformation (max 91 µm).…”

Fault Diagnosis of PMSMs Based on Image Features of Multi-Sensor Fusion

“…In the feature extraction stage of traditional motor fault diagnosis research, signal processing methods, including time-domain [12][13][14], frequency domain [5,[15][16][17][18], and time-frequency domain [19][20][21][22][23], are commonly employed to analyze the measured signals and extract fault features associated with different states. However, the above methods often have problems of low fault diagnosis accuracy and a wide range of applications, and the related research has the limitation of extracting the detailed features of the signals in a single dimension only.…”