Design of an integrated system for on-line test and diagnosis of rotary actuators

In this paper, the design of an on-chip Fault Detection and Diagnosis System for Condition Based Maintenance of electromechanical actuators is presented. The proposed system is based on signal processing algorithms integrated in a customized Application Specific Integrated Circuit (ASIC). The design was synthesized using a 90nm CMOS standard cell library. As a case study, post-synthesis simulations were performed using signals acquired from a real electromechanical valve, using torque and vibration sensors considering both fault-free and defective situations for the actuator. Results show the effectiveness of the system in performing real-time fault detection and identification, with low power consumption and low silicon area utilization

Automobile maintenance modelling using gcForest

Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost

Novel deep cross-domain framework for fault diagnosis or rotary machinery in prognostics and health management

Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This doctoral research provides a systematic review of state-of-the-art deep learning-based PHM frameworks, an empirical analysis on bearing fault diagnosis benchmarks, and a novel multi-source domain adaptation framework. It emphasizes the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. Besides, the limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. The empirical study of the benchmarks highlights the evaluation results of the existing models on bearing fault diagnosis benchmark datasets in terms of various performance metrics such as accuracy and training time. The result of the study is very important for comparing or testing new models. A novel multi-source domain adaptation framework for fault diagnosis of rotary machinery is also proposed, which aligns the domains in both feature-level and task-level. The proposed framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Besides, the model can be easily reduced to a single-source domain adaptation problem. Also, the model can be readily updated to unsupervised domain adaptation problems in other fields such as image classification and image segmentation. Further, the proposed model is modified with a novel conditional weighting mechanism that aligns the class-conditional probability of the domains and reduces the effect of irrelevant source domain which is a critical issue in multi-source domain adaptation algorithms. The experimental verification results show the superiority of the proposed framework over state-of-the-art multi-source domain-adaptation models

Desenvolvimento de um sistema em chip de processamento online para manutenção inteligente

Estratégias de monitoramento, baseadas na análise da condição de equipamentos utilizando ferramentas de processamento digital de sinais, inteligência artificial e tolerância a falhas, tornam-se cada vez mais necessárias nos processos industriais. As técnicas de manutenção inteligente conferem confiabilidade, disponibilidade e eficácia, e são estudadas, neste trabalho, no atual estado da arte. Porém, grande parte delas utiliza medidas com estados e parâmetros do processo que são dispendiosas e envolvem elevado tempo de amostragem e análise. O objetivo deste trabalho é desenvolver um novo sistema capaz de estimar a condição de saúde de um equipamento a partir das leituras de vibração e torque de sensores, e assim, viabilizar a detecção, predição e identificação de falhas online em atuadores elétricos utilizados em linhas de transporte de petróleo e/ou derivados. Para isso, foi desenvolvida uma técnica que, por meio de um dispositivo computacional, possibilita monitorar, considerando ruído e, de forma interativa, as variações dos parâmetros de um processo físico, tais como: falhas abruptas, incipientes e intermitentes. Isso corresponde às atividades de detecção, identificação de falhas e previsões sobre possíveis problemas que venham a surgir em consequência de pequenos desvios do comportamento normal do sistema. A metodologia empregada é baseada na estrutura do modelo Open Systems Architecture for Condition-Based Maintenance (OSA-CBM), que permite atuar nas seguintes camadas: 1) Aquisição de dados; 2) Manipulação de dados; 3) Monitoramento das condições; 4) Avaliação da saúde O sistema compreende a análise simultânea das propriedades de tempo e frequência do sinal, extração de características e filtragem adaptativa. Uma bancada de testes foi utilizada para reproduzir algumas falhas típicas que podem causar degradação na operação de atuadores fabricados no mercado. O sistema foi denominado Fault Detection System (FDS) e é baseado em técnicas de processamento de sinais que tem como saída um sinal de resíduo ou erro quando na ocorrência de uma falha correspondente nos equipamentos monitorados. A versão em software do sistema foi registrada no Instituto Nacional da Propriedade Industrial (INPI) no "BR 51 2016 000863-6". Uma nova versão para prototipagem em hardware do FDS em conjunto com um bloco auxiliar denominado Fault Detection Index (FDI), que também é proposto neste trabalho, foi desenvolvido na linguagem Verilog e implementado utilizando uma biblioteca Complementary Metal-Oxide-Semiconductor (CMOS) de 90 nm visando baixo consumo de energia ( 654 μW), baixa utilização de área em silício ( 0, 14 mm2) e processamento em tempo real. Os resultados demonstram a eficácia do método de detecção, diagnóstico e identificação de falhas apresentadas em atuadores elétricos empregados para controle de válvulas.Monitoring strategies based on the analysis of equipment condition with information derived from digital signal processing, artificial intelligence and fault tolerance tools become increasingly necessary in industrial process. In this context, intelligent maintenance techniques provide reliability, availability and are being increasingly studied in the current state of the art researches. However, most of them are based on measurements with states and process parameters that are costly and involve high sampling and analysis time. In order to avoid this problem, this work presents a new system capable of estimating the health condition of an equipment from the vibration and torque measurements of sensors, thus enabling online detection, prediction and identification of faults in electric actuators. The developed system represents a technique that, by means of a computational device, allows to monitor the variations of the parameters of a physical process such as abrupt, incipient and intermittent failures. This corresponds to the activities of fault detection, identification and prediction of possible problems that may arise due to minor deviations of the normal behavior state of the system. The methodology is based on the Open Systems Architecture for Condition-Based Maintenance (OSA-CBM) framework, which allows to act in the following layers: 1) Data acquisition; 2) Data manipulation; 3) Condition monitoring; 4) Health assessment. The system comprises the simultaneous analysis of signal time and frequency properties, feature extraction and adaptive filtering A testbench structure has been used to reproduce some typical faults that can cause degradation in the operation of the available commercial actuators. The results show the effectiveness of the method of detection, diagnosis and identification of faults that may occur in electric valves. The system is denominated Fault Detection System (FDS) and it is based on digital signal processing techniques producing a residue signal or error in the occurrence of a corresponding fault in the monitored equipment. A software version of the system was registered with the Instituto Nacional da Propriedade Industrial (INPI) no "BR 51 2016 000863-6". A new version for hardware prototyping of FDS together with the Fault Detection Index (FDI), which is also proposed in this work, was using Ver- ilog language and implemented in a 90 nm Complementary Metal-Oxide-Semiconductor (CMOS) library for low power consumption ( 654 μW), low silicon area utilization ( 0.14 mm2) and real time processing. The results demonstrate the effectiveness of the method of detection, diagnosis and identification of faults present in electric actuators used for controling fluidic valves