Adaptive Multiscale Weighted Permutation Entropy for Rolling Bearing Fault Diagnosis

© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.Bearing vibration signals contain non-linear and non-stationary features due to instantaneous variations in the operation of rotating machinery. It is important to characterize and analyze the complexity change of the bearing vibration signals so that bearing health conditions can be accurately identified. Entropy measures are non-linear indicators that are applicable to the time series complexity analysis for machine fault diagnosis. In this paper, an improved entropy measure, termed Adaptive Multiscale Weighted Permutation Entropy (AMWPE), is proposed. Then, a new rolling bearing fault diagnosis method is developed based on the AMWPE and multi-class SVM. For comparison, experimental bearing data are analyzed using the AMWPE, compared with the conventional entropy measures, where a multi-class SVM is adopted for fault type classification. Moreover, the robustness of different entropy measures is further studied for the analysis of noisy signals with various Signal-to-Noise Ratios (SNRs). The experimental results have demonstrated the effectiveness of the proposed method in fault diagnosis of rolling bearing under different fault types, severity degrees, and SNR levels.Peer reviewedFinal Accepted Versio

Fault Diagnosis and Failure Prognostics of Lithium-ion Battery based on Least Squares Support Vector Machine and Memory Particle Filter Framework

123456A novel data driven approach is developed for fault diagnosis and remaining useful life (RUL) prognostics for lithium-ion batteries using Least Square Support Vector Machine (LS-SVM) and Memory-Particle Filter (M-PF). Unlike traditional data-driven models for capacity fault diagnosis and failure prognosis, which require multidimensional physical characteristics, the proposed algorithm uses only two variables: Energy Efficiency (EE), and Work Temperature. The aim of this novel framework is to improve the accuracy of incipient and abrupt faults diagnosis and failure prognosis. First, the LSSVM is used to generate residual signal based on capacity fade trends of the Li-ion batteries. Second, adaptive threshold model is developed based on several factors including input, output model error, disturbance, and drift parameter. The adaptive threshold is used to tackle the shortcoming of a fixed threshold. Third, the M-PF is proposed as the new method for failure prognostic to determine Remaining Useful Life (RUL). The M-PF is based on the assumption of the availability of real-time observation and historical data, where the historical failure data can be used instead of the physical failure model within the particle filter. The feasibility of the framework is validated using Li-ion battery prognostic data obtained from the National Aeronautic and Space Administration (NASA) Ames Prognostic Center of Excellence (PCoE). The experimental results show the following: (1) fewer data dimensions for the input data are required compared to traditional empirical models; (2) the proposed diagnostic approach provides an effective way of diagnosing Li-ion battery fault; (3) the proposed prognostic approach can predict the RUL of Li-ion batteries with small error, and has high prediction accuracy; and, (4) the proposed prognostic approach shows that historical failure data can be used instead of a physical failure model in the particle filter

Structural damage monitoring based on machine learning and bio-inspired computing

For a few decades, systems for supervising structures have become increasingly irnportant. In origin, the strategies had as a goal only the detection of damages. Furthermore, now monitor­ing the civil or military structures permanently and offering sufficient and relevant information helping make the right decisions. The SHM is applicable, carrying out preventive or corrective maintenance decisions, reducing the possibility of accidents, and promoting the reduction of costs that more extensive repairs imply when the damage is detected early. The current work focused on three elements of diagnosis of structural damage: detection, classification, and loca­tion, either in metaltic or cornposite material structures, given their wide use in air, land, rnar­itime transport vehicles, aerospace, wind turbines, civil and military infrastructure. This work used the tools offered by machine leaming and bio-inspired computing. Given the right results to solve complex problems and recognizing pattems. It also involves changes in temperature since it is one of the parameters that influence real environments' structures. Information of a statistical nature applied to recognizing pattems and reducing the size of the information was used with tools such as PCA (principal component analysis), thanks to the experience obtained in works developed by the CoDAlab research group. The document is divided into five parts. The first includes a general description of the problem, the objecti.-es, and the results obtained, in addition to a brief theoretical introduction. Chapters 2, 3, and 4 include articles published in different joumals. Chapter 5 shows the results and conclusions. Other contributions, such as a book chapter and sorne papers presented at conferences, are included in appendix A. Finally, appendix B presents a multiplexing system used to develop the experiments carried out in this work.Desde hace algunas décadas los sistemas para supervisar estructuras han tenido cada vez más relevancia. En esta evolución se ha pasado de estrategias que tenían como meta sólo la detec­ción de fallas a otras que buscan monitorizar permanentemente las estructuras bien sean éstas civiles o militares, ofreciendo información suficiente y pertinente que incide positivamente en el momento de tomar buenas decisiones, dentro de las cuales cabe destacar por ejemplo, las ori­entadas a realizar mantenimientos preventivos o correctivos si es del caso, reduciendo la posi­bilidad de accidentes, además de propiciar la disminución de costos que implican las repara­ciones más extensas cuando el daño se logra detectar de manera temprana. El presente trabajo se enfocó en tres elementos de diagnóstico de daños en estructuras, siendo estos en particular la detección, clasificación y localización, bien sea en estructuras metálicas o de material com­puesto, dado su amplio uso en vehículos de transporte aéreo, terrestre, marítimo, aeroespacial, aerogeneradores, infraestructura civil y militar. Se utilizaron las herramientas que ofrecen el aprendizaje automático (machine leaming) y la computación bio-inspirada, dados los buenos resultados que han ofrecido en la solución de problemas complejos y el reconocimiento de pa­trones. Involucrando cambios de temperatura dado que es uno de los parámetros a los que se ven enfrentadas las estructuras en ambientes reales. Se utilizó información de naturaleza estadística aplicada al reconocimiento de patrones y reducción del tamaño de la información con herramientas como el PCA (análisis de componentes principales), gracias a la experiencia lograda en trabajos desarrollados por el grupo de investigación CoDAlab. El documento está dividido en cinco capítulos. En el primerio se incluye una descripción general del problema, los objetivos y los resultados obtenidos, además de un breve introduc­ción teórica. Los Capítulos 2,3 y 4 incluyen los artículos publicados en diferentes revistas. En el Capítulo 5 se realiza una presentación de los resultados y conclusiones. En el Anexo A se incluyen otras contribuciones tales como un capítulo de libro y algunos trabajos presentados en conferencias. Finalmente en el anexo B se presenta el diseño de un sistema de multipliexación utilizado en el desarrollo de los experimentos realizados en el presente trabajo.Postprint (published version