A5011135544- Machine Fault Diagnosis Techniques
- Fault Detection and Control Systems
- Gear and Bearing Dynamics Analysis
- Engineering Diagnostics and Reliability
- Multi-Agent Systems and Negotiation
- Scheduling and Optimization Algorithms
- Oil and Gas Production Techniques
- Advanced machining processes and optimization
- Hydraulic and Pneumatic Systems
- Business Process Modeling and Analysis
- Fuzzy Logic and Control Systems
- Industrial Vision Systems and Defect Detection
- Neural Networks and Applications
- Evolutionary Algorithms and Applications
- Anomaly Detection Techniques and Applications
- Metaheuristic Optimization Algorithms Research
- Spectroscopy and Chemometric Analyses
- Structural Health Monitoring Techniques
- Flexible and Reconfigurable Manufacturing Systems
- Advanced Data Processing Techniques
- Industrial Technology and Control Systems
- Semantic Web and Ontologies
- Refrigeration and Air Conditioning Technologies
- Mineral Processing and Grinding
- Time Series Analysis and Forecasting
Politecnica Salesiana University
2016-2025
University of the Andes
2005-2019
University of Cuenca
2017
Université de Pau et des Pays de l'Adour
2017
Universidad de Los Andes
2006-2014
Central University
2013
Universidad Nueva Esparta
2013
Hospital de Clínicas
2013
Universidad de Los Andes
2005
Fault diagnosis is important for the maintenance of rotating machinery. The detection faults and fault patterns a challenging part machinery diagnosis. To tackle this problem, model deep statistical feature learning from vibration measurements presented in paper. Vibration sensor signals collected mechanical systems are represented time, frequency, time-frequency domains, each which then used to produce set. For features, real-value Gaussian-Bernoulli restricted Boltzmann machines (GRBMs)...
Gearboxes and bearings play an important role in industries for motion torque transmission machines. Therefore, early diagnoses are sought to avoid unplanned shutdowns, catastrophic damage the machine or human losses; additionally, appropriate diagnosis contributes increase productivi ty reduce maintenance costs. This paper addresses a methodological framework of multi-faults rotating machinery through use features rankings. The classification uses K nearest neighbors random forest, based on...
There are growing demands for condition-based monitoring of gearboxes, and techniques to improve the reliability, effectiveness accuracy fault diagnosis considered valuable contributions. Feature selection is still an important aspect in machine learning-based order reach good performance system. The main aim this research propose a multi-stage feature mechanism selecting best set condition parameters on time, frequency time-frequency domains, which extracted from vibration signals purposes...
Bearings are one of the most omnipresent and vulnerable components in rotary machinery such as motors, generators, gearboxes, or wind turbines. The consequences a bearing fault range from production losses to critical safety issues. To mitigate these condition based maintenance is g aining momentum. This on variety diagnosis techniques where fuzzy clustering plays an important role it can be used detection, classification, prognosis. A algorithms have been proposed applied this context....
At present, countless approaches to fault diagnosis in reciprocating machines have been proposed, all considering that the available machinery dataset is equal proportions for conditions. However, when application closer reality, problem of data imbalance increasingly evident. In this paper, we propose a method creation diagnoses consider an extreme data. Our approach first processes vibration signals machine using wavelet packet transform-based feature-extraction stage. Then, improved...
Gearboxes are widely used in industrial processes as mechanical power transmission systems. Then, gearbox failures can affect other parts of the system and produce economic loss. The early detection possible failure modes their severity assessment such devices is an important field research. Data-driven approaches usually require exhaustive development pipelines including models’ parameter optimization feature selection. This paper takes advantage recent Auto Machine Learning (AutoML) tools...
Compressors and pumps are machines frequently used in petroleum chemical industries for fluid transportation through flow systems to keep industrial processes running permanently. As their failure can produce costly disruption, developing fault detection diagnosis tools is essential accurately detecting diagnosing faults. This research proposes a bi-dimensional representation of the vibration signal corresponding Mel Frequency Cepstral Coefficients (MFCC) first two derivatives as features....