Abstract
An effective bearing fault detection and diagnosis (FDD) model is important for ensuring the normal and safe operation of machines. This paper presents a reliable model-reference observer technique for FDD based on modeling of a bearing’s vibration data by analyzing the dynamic properties of the bearing and a higher-order super-twisting sliding mode observation (HOSTSMO) technique for making diagnostic decisions using these data models. The HOSTSMO technique can adaptively improve the performance of estimating nonlinear failures in rolling element bearings (REBs) over a linear approach by modeling 5 degrees of freedom under normal and faulty conditions. The effectiveness of the proposed technique is evaluated using a vibration dataset provided by Case Western Reserve University, which consists of vibration acceleration signals recorded for REBs with inner, outer, ball, and no faults, i.e., normal. Experimental results indicate that the proposed technique outperforms the ARX-Laguerre proportional integral observation (ALPIO) technique, yielding 18.82%, 16.825%, and 17.44% performance improvements for three levels of crack severity of 0.007, 0.014, and 0.021 inches, respectively.
Highlights
Rolling element bearings (REBs) are very important components in rotating machines, as they are used to reduce the friction between moving parts for linear and rotational motion [1]
Several types of faults have been defined in rolling element bearings (REBs), which are divided into four main categories, i.e., inner raceway faults, outer raceway faults, ball faults, and cage faults [3]
This paper presented a nonlinear observation-based bearing fault diagnosis technique using a higher-order super-twisting sliding mode observation method
Summary
Rolling element bearings (REBs) are very important components in rotating machines, as they are used to reduce the friction between moving parts for linear and rotational motion [1]. Complexities of the tasks and nonlinear parameters in REBs make their fault detection and diagnosis (FDD) very challenging. Different techniques have been introduced for the diagnosis of faults in bearings, including signal-based fault diagnosis [4,5,6,7,8,9], knowledge-based fault diagnosis [10,11], model-based fault diagnosis [12,13,14], and hybrid/active approaches to fault diagnosis [15,16]. Signal-based fault diagnosis has several advantages, this method has challenges associated with system reliability in the presence of uncertainty and external disturbances. Various model-based methods have been rigorously studied in the Sensors 2018, 18, 1128; doi:10.3390/s18041128 www.mdpi.com/journal/sensors
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