Bearing Fault Diagnosis under Time-Varying Speed and Load Conditions via Observer-Based Load Torque Analysis

Abstract

Bearing fault is the most common failure in rotating machines, and bearing fault diagnosis (BFD) has been investigated using vibration, current, or acoustic signals. However, there are still challenges in some existing approaches. This study proposes a novel BFD method based on natural observer. Based on the analysis of the effects on the load torque signal caused by bearing faults in the permanent magnetic synchronous machine (PMSM), a modified natural observer was designed to reconstruct the load torque signal from electrical signals, acquiring a novel indicator without the additional sensor installed. Angular resampling was implemented to convert the non-stationary load torque signal into a stationary one to reduce the computational complexity. For full-auto diagnosis without human involvement, a threshold determination algorithm was also modified. Experimental validations were carried out under speed-varying and torque-varying conditions and were compared with phase current and q-axis current signals. The average signal-to-noise ratio (SNR) of the estimated load torque is about 8.65 times compared with the SNR of the traditional q-axis current. The effectiveness of the proposed method prior to the traditional PMSM bearing fault indicators is demonstrated by the order spectrum results.

Classification of Optoelectronic Rotary Encoder Faults Based on Deep Learning Methods in Permanent Magnet Synchronous Motor Drive System

“…The current values in the q-axis were chosen because previous research showed the high effectiveness of simple failure detectors, based on state variable observers, using this signal [33]. The full input vector for experimental (10) and simulation studies (11) is shown below:…”

“…Both the motor and drive control system are subject to diagnostics. A significant number of research works focus on detecting and classifying damages to the stator [6][7][8] or motor bearings [9][10][11]. The topic of frequency converter diagnostics is also well-developed and described in the literature [12][13][14].…”

Fault diagnosis-based observers using Kalman filters and Luenberger estimators: Application to the pitch system fault actuators

“…Whereas, authors in [27] have proposed a Kalman filter to estimate the process parameters with an adaptive fuzzy inference system. Papers [25,28] present adaptive faulttolerant control (FTC) methodologies based observers for sensors and actuators under timevarying speeds for the wind turbine benchmark. Luenberger Observer also is an interesting estimator for linear systems [25], can be used for fault detection and diagnosis [24].…”

A Novel Mode Un-Mixing Approach in Variational Mode Decomposition for Fault Detection in Wound Rotor Induction Machines

“…Many sensors can collect signals and data for condition monitoring of IMs and fault detection. These sensors can measure various parameters such as voltage [9] and current [10][11][12], stray flux [13,14], singular value decomposition of the stator current [15], load torque analysis [16], neural network-based detection [17], etc. Some of these methods are invasive and rarely noticed.…”