Diagnosis electromechanical system by means CNN and SAE: an interpretable-learning study

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Cyber-physical systems are the response to the adaptability, scalability and accurate demands of the new era of manufacturing called Industry 4.0. They will become the core technology of control and monitoring in smart manufacturing processes. In this regard, the complexity of industrial systems implies a challenge for the implementation of monitoring and diagnosis schemes. Moreover, the challenges that is presented in technological aspects regarding connectivity, data management and computing are being resolved through different IT-OT (information technology and operational technology) convergence proposals. These solutions are making it possible to have large computing capacities and low response latency. However, regarding the logical part of information processing and analysis, this still requires additional studies to identify the options with a better complexity-performance trade-off. The emergence of techniques based on artificial intelligence, especially those based on deep-learning, has provided monitoring schemes with the capacity for characterization and recognition in front of complex electromechanical systems. However, most deep learning-based schemes suffer from critical lack of interpretability lying to low generalization capabilities and overfitted responses. This paper proposes a study of two of the main deep learning-based techniques applied to fault diagnosis in electromechanical systems. An analysis of the interpretability of the learning processes is carried out, and the approaches are evaluated under common performance metrics.Peer ReviewedPostprint (published version

Deep-compact-clustering based anomaly detection applied to electromechanical industrial systems

The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater challenge, since uncharacterized catastrophic faults can occur. However, the existing methods for anomaly detection present limitations when dealing with highly complex industrial systems. For that purpose, a novel fault diagnosis methodology is developed to face the anomaly detection. An unsupervised anomaly detection framework named deep-autoencoder-compact-clustering one-class support-vector machine (DAECC-OC-SVM) is presented, which aims to incorporate the advantages of automatically learnt representation by deep neural network to improved anomaly detection performance. The method combines the training of a deep-autoencoder with clustering compact model and a one-class support-vector-machine function-based outlier detection method. The addressed methodology is applied on a public rolling bearing faults experimental test bench and on multi-fault experimental test bench. The results show that the proposed methodology it is able to accurately to detect unknown defects, outperforming other state-of-the-art methods.Peer ReviewedPostprint (published version

Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings

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.Peer ReviewedPostprint (published version

On the use of context information for an improved application of data-based algorithms in condition monitoring

xi, 124 p.En el campo de la monitorización de la condición, los algoritmos basados en datos cuentan con un amplio recorrido. Desde el uso de los gráficos de control de calidad que se llevan empleando durante casi un siglo a técnicas de mayor complejidad como las redes neuronales o máquinas de soporte vectorial que se emplean para detección, diagnóstico y estimación de vida remanente de los equipos. Sin embargo, la puesta en producción de los algoritmos de monitorización requiere de un estudio exhaustivo de un factor que es a menudo obviado por otros trabajos de la literatura: el contexto. El contexto, que en este trabajo es considerado como el conjunto de factores que influencian la monitorización de un bien, tiene un gran impacto en la algoritmia de monitorización y su aplicación final. Por este motivo, es el objeto de estudio de esta tesis en la que se han analizado tres casos de uso. Se ha profundizado en sus respectivos contextos, tratando de generalizar a la problemática habitual en la monitorización de maquinaria industrial, y se ha abordado dicha problemática de monitorización de forma que solucionen el contexto en lugar de cada caso de uso. Así, el conocimiento adquirido durante el desarrollo de las soluciones puede ser transferido a otros casos de uso que cuenten con contextos similares

On the use of context information for an improved application of data-based algorithms in condition monitoring

xi, 124 p.En el campo de la monitorización de la condición, los algoritmos basados en datos cuentan con un amplio recorrido. Desde el uso de los gráficos de control de calidad que se llevan empleando durante casi un siglo a técnicas de mayor complejidad como las redes neuronales o máquinas de soporte vectorial que se emplean para detección, diagnóstico y estimación de vida remanente de los equipos. Sin embargo, la puesta en producción de los algoritmos de monitorización requiere de un estudio exhaustivo de un factor que es a menudo obviado por otros trabajos de la literatura: el contexto. El contexto, que en este trabajo es considerado como el conjunto de factores que influencian la monitorización de un bien, tiene un gran impacto en la algoritmia de monitorización y su aplicación final. Por este motivo, es el objeto de estudio de esta tesis en la que se han analizado tres casos de uso. Se ha profundizado en sus respectivos contextos, tratando de generalizar a la problemática habitual en la monitorización de maquinaria industrial, y se ha abordado dicha problemática de monitorización de forma que solucionen el contexto en lugar de cada caso de uso. Así, el conocimiento adquirido durante el desarrollo de las soluciones puede ser transferido a otros casos de uso que cuenten con contextos similares

Artificial intelligence applied to electromechanical monitoring, a performance analysis

Artificial intelligence is a wide concept and it’s being used in more and more machine applications, teaching them to perform tasks which would require human intelligence. The implemented algorithm requires samples of ''experience'' from which it can learn and predict the outcome; from there it mainly feeds itself. AI is basically divided into two subsets; deep learning and machine learning. They are mostly distinguished with the way the data is presented to the network. To summarize this project; First the data was monitored on an electromechanical system, monitored data from vibrations was saved and brought in Matlab program, there the data was transformed for future use with autoencoders, which learned the conditions, after training we have to try different parameters for getting the closest reconstruction possible. In the project we applied several techniques like using a multilayered auto-encoder and then finding the best hyper parameters for best results (they were measured by plotting signals and mean square error).Incomin

Novel Methods Based on Deep Learning Applied to Condition Monitoring in Smart Manufacturing Processes

The Industry 4.0 is the recent trend of automation and the rotating machinery takes a role of great relevance when it comes to meet the demands and challenges of smart manufacturing. Condition-based monitoring (CBM) schemes are the most prominent tool to cover the task of predictive diagnosis. With the current demand of the industry and the increasing complexity of the systems, it is vital to incorporate CBM methodologies that are capable of facing the variability and complexity of manufacturing processes. In recent years, various deep learning techniques have been applied successfully in different areas of research, such as image recognition, robotics, and the detection of abnormalities in clinical studies; some of these techniques have been approaching to the diagnosis of the condition in rotating machinery, promising great results in the Industry 4.0 era. In this chapter, some of the deep learning techniques that promise to make important advances in the field of intelligent fault diagnosis in industrial electromechanical systems will be addressed

Virtual twins of nonlinear vibrating multiphysics microstructures: physics-based versus deep learning-based approaches

Micro-Electro-Mechanical-Systems are complex structures, often involving nonlinearites of geometric and multiphysics nature, that are used as sensors and actuators in countless applications. Starting from full-order representations, we apply deep learning techniques to generate accurate, efficient and real-time reduced order models to be used as virtual twin for the simulation and optimization of higher-level complex systems. We extensively test the reliability of the proposed procedures on micromirrors, arches and gyroscopes, also displaying intricate dynamical evolutions like internal resonances. In particular, we discuss the accuracy of the deep learning technique and its ability to replicate and converge to the invariant manifolds predicted using the recently developed direct parametrization approach that allows extracting the nonlinear normal modes of large finite element models. Finally, by addressing an electromechanical gyroscope, we show that the non-intrusive deep learning approach generalizes easily to complex multiphysics problem

Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches

Electric motor condition monitoring can detect anomalies in the motor performance which have the potential to result in unexpected failure and financial loss. This study examines different fault detection and diagnosis approaches in induction motors and is presented in six chapters. First, an anomaly technique or outlier detection is applied to increase the accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability by using one-class classification technique. Then, ensemble-based anomaly detection is utilized to compare different methods in ensemble learning in detection of broken rotor bars. Finally, a deep neural network is developed to extract significant features to be used as input parameters of the network. Deep autoencoder is then employed to build an advanced model to make predictions of broken rotor bars and bearing faults occurring in induction motors with a high accuracy