Diagnosis Methodology Based on Deep Feature Learning for Fault Identification in Metallic, Hybrid and Ceramic Bearings

Abstract

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.

Deep-Learning Method Based on 1D Convolutional Neural Network for Intelligent Fault Diagnosis of Rotating Machines

“…The third investigation reported in [36] is a deep auto-encoder method with fusing discriminant, where the IMS dataset is classified with an accuracy rate of 98.3%. Similar research is proposed in [37], where a methodology based on Deep Feature Learning for fault identification is applied. Each sample consists of 3000 data points; where a multi-domain feature calculation is applied, the accuracy is 99.8%.…”

Multisensor Data Fusion for Localization of Pollution Sources in Wastewater Networks

“…The last one is discovering the flow path of the pollutant that leads to easier source localization. Data fusion has also proved to be a viable technique for knowledge discovery in other fields such as bearing fault identification [ 23 ].…”

Multistage Centrifugal Pump Fault Diagnosis Using Informative Ratio Principal Component Analysis

“…SF can be extracted from the VS in time, frequency, and TFD [ 23 ]. The power of deep learning techniques can be utilized for fault-related discriminant feature extraction and classification [ 24 , 25 ]. Juan et al [ 24 ] proposed a data-driven fault diagnosis strategy for bearing fault diagnosis.…”

“…The power of deep learning techniques can be utilized for fault-related discriminant feature extraction and classification [ 24 , 25 ]. Juan et al [ 24 ] proposed a data-driven fault diagnosis strategy for bearing fault diagnosis. The proposed method extracts SF’s from the raw vibration signal in the time domain, frequency domain, and TFD.…”

“…The filtered mode of vibration forms the FSFB, which is then used for discriminant SF’s extraction in time, frequency, and TFD. Yet the study presented in [ 24 ] is interesting and can be used for CP fault diagnosis after VS pre-processing. The SF’s extracted from time, frequency, and TFD are combined into a single feature vector called MDFP.…”