Abstract

In monitoring and supervision schemes, fault detection and diagnosis characterize high efficiency and quality production systems. To achieve such properties, these structures are based on techniques that allow detection and diagnosis of failures in real time. Detection signals faults and diagnostics provide the root cause and location. Fault detection is based on signal and process mathematical models, while fault diagnosis is focused on systems theory and process modeling. Monitoring and supervision complement each other in fault management, thus enabling normal and continuous operation. Its application avoids stopping productive processes by early detection of failures and by applying real-time actions to eliminate them, such as predictive and proactive maintenance based on process conditions. The integration of all these methodologies enables intelligent monitoring and supervision systems, enabling real-time fault detection and diagnosis. Their high performance is associated with statistical decision-making techniques, expert systems, artificial neural networks, fuzzy logic and computational procedures, making them efficient and fully autonomous in making decisions in the real-time operation of a production system.

Highlights

  • Advances in production techniques have improved the capacity of the productive systems of the industries, since the equipment used in these processes have improved their reliability and availability in the operation, making the productive processes more efficient

  • A comparison process is carried out with a previously defined reference value. This revision is performed in real time and its result compared to the past behavior, this will allow defining if it is really presenting an abnormality or it is an isolated eventuality

  • The behavior history is analyzed and an image of the state of the selected parameters is created [8, 9]. This image is compared to the behavior of these same parameters in real time

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Summary

Introduction

Advances in production techniques have improved the capacity of the productive systems of the industries, since the equipment used in these processes have improved their reliability and availability in the operation, making the productive processes more efficient. Since the life cycle stages of production process equipment require high investments, and maintenance and operation procedures to achieve appropriate return times on the investments made, must ensure high availability and reliability rates These performance indexes are improved by reducing the number of failures and managing their severities, while ensuring an increase in overall security. Among the existing model-based fault diagnosis schemes, the so-called observations-based technique has received much attention since the 1990s This technique was developed within the framework of the successful theory of advanced control, where powerful tools are available to design or to extrapolate recorded observations through efficient and reliable algorithms for data processing in order to reconstruct process variables. An application-oriented approach will be done with methods that have proven their proper performance in practical applications

Monitoring and supervision of systems
Fault diagnosis monitoring systems
Main features of automatic fault diagnostic systems
Structuring of monitoring and supervision systems in an intelligent system
Monitoring
Supervision
Evaluation
Diagnosis
Fault detection methods
Applying corrective actions: alternatives
Blackboard
Inference machine
Neural networks
Fuzzy logic
Solution
Predictive and proactive maintenance
Innovation
Benefits
Conclusions
Full Text
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