**1. 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.

One of the most critical questions about automated system design today is reliability and availability of a system. A traditional way to improve the reliability and availability of systems is to improve the quality, reliability, and robustness of the individual components of such a system, such as such as sensors, actuators, controllers and/or computers, used integrally in modern monitoring processes. Even so, a fault-free operation cannot be guaranteed. Process monitoring and fault diagnosis

are a vital part of the innovative and modern systems of automatic management of the operation of production systems [1, 2].

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.

To achieve these goals, two important techniques are available that allow optimized maintenance management, known as predictive and proactive, which are complemented by the techniques: corrective and preventive. This set of techniques offers its best results through the implementation of efficient real-time monitoring and supervision structures, making production systems highly reliable in supplying their products and in the quality of products offered. Corrective maintenance corrects the problem, preventive maintenance prevents the problem.

On the other hand, predictive maintenance consists in the frequent measurement of physical quantities, considered representative and through the analysis of their behavior, to extract their state or operative condition. This allows to suggest the most appropriate moment to apply the necessary actions in the equipments that present characteristics of being in the initial state of a fault - early failure (the root cause is slightly impacting the equipment continuously), anticipating in this way to the emergence of a serious system failure. The predictive maintenance process allows obtaining a report on the operational condition of the equipment. This process of issuing the report basically comprises four stages:


In traditional predictive maintenance processes, all these steps are performed manually. Alternatively, these steps can be performed using computer systems that allow automating this process is called Systems for Automatic Fault Diagnosis [3].

As can be inferred, the selection, implementation, operation and maintenance of a System for Automatic Diagnosis of Failures is not a simple task, requiring at each stage, care so that the result provided by the system, after its implementation, is within the one initially specified. For this, it is necessary to use appropriate tools and strategies, in each step, in order to maximize the success in executing each of them.

Proactive maintenance is a procedure that minimizes the impact of lack of maintenance or reduced maintenance on the equipment of a production system and also by its own characteristics complements the other maintenance techniques. The main action of this maintenance is to analyze the performance indicators and identify the root cause of the failures, the degradation of the equipment and to remove them before the severity of a fault itself increases [4].

In this chapter, a description will be given of the various methodologies for converting an online monitoring and supervision system into an intelligent system that allows the detection and diagnosis of failures, training it to assure autonomy in taking the necessary actions in real time to avoid them and seek their causes to eliminate them.

The proposed content has two basic objectives: to discuss some important factors for the success in the implantation and use of these structures or systems, as

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*Real-Time Fault Detection and Diagnosis Using Intelligent Monitoring and Supervision Systems*

well as the main benefits in the integrated and simultaneous use of the monitoring and supervision of several physical quantities of the equipment, with the goal of

The technological development in this area has allowed the emergence of innovative methodologies for the detection and diagnosis of failures. The failure detection method recognizes that the failure has occurred, and fault diagnosis finds the root cause and location of that failure. In general, fault detection methods are based on mathematical models of signal and process, and on methods of systems theory and process modeling to generate fault symptoms. Fault diagnosis methods use causal relationships between fault and symptom, applying statistical decision

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

The content described here is intended to provide an introduction to advanced monitoring and supervision, focused as a framework or intelligent assembly for fault detection and diagnostics [1, 6], and fault-tolerant systems especially for

In general, almost all physical signals are continuous, for example, position and velocity of a body, speech or music picked up by a microphone, voltage or current in

The sampling (instantaneous) of an analog signal or waveform is the process by which the signal is represented by a discrete set of numbers. These numbers, or samples, are equal to the signal value at well-determined instants (the sampling times). Samples must be obtained in such a way that it is possible to reconstruct the signal accurately. That is, the original waveform, defined in "continuous" time, is represented in "discrete" time by samples obtained at conveniently spaced sam-

An application-oriented approach will also be done with methods that have

The monitoring and supervision of processes aim to show the real state of the equipment involved in a productive process, indicating undesirable or illicit states and the appearance of a change in its initial phase (early failure). This situation will require taking appropriate and immediate action to avoid catastrophic damage in

Deviations from the normal behavior of the parameters of an equipment or system arise from faults and/or errors, which can be attributed to several causes. These changes are symptoms of possible early failure, and if the necessary actions are not taken to eliminate them, they may become actual failures that may compromise the performance of productive systems. The justification for monitoring and supervision systems is to avoid such defects or failures in systems by collecting continuous information (provided by the monitoring system) in real time, on the behavior of the equipment of a production system and its supervision data) that will allow you

to determine if a device or equipment is operating normally or at risk.

Deviations from the normal behavior of the parameters of an equipment or system arise from faults and/or errors, which can be attributed to several causes.

proven their proper performance in practical applications.

**2. Monitoring and supervision of systems**

*DOI: http://dx.doi.org/10.5772/intechopen.90158*

process variables.

an electric circuit.

pling instants.

the future.

increasing the "accuracy" of fault detection and diagnosis.

methods, artificial intelligence and computational software [5].

processes characterized by continuous and sampled (discrete) signals.

#### *Real-Time Fault Detection and Diagnosis Using Intelligent Monitoring and Supervision Systems DOI: http://dx.doi.org/10.5772/intechopen.90158*

well as the main benefits in the integrated and simultaneous use of the monitoring and supervision of several physical quantities of the equipment, with the goal of increasing the "accuracy" of fault detection and diagnosis.

The technological development in this area has allowed the emergence of innovative methodologies for the detection and diagnosis of failures. The failure detection method recognizes that the failure has occurred, and fault diagnosis finds the root cause and location of that failure. In general, fault detection methods are based on mathematical models of signal and process, and on methods of systems theory and process modeling to generate fault symptoms. Fault diagnosis methods use causal relationships between fault and symptom, applying statistical decision methods, artificial intelligence and computational software [5].

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.

The content described here is intended to provide an introduction to advanced monitoring and supervision, focused as a framework or intelligent assembly for fault detection and diagnostics [1, 6], and fault-tolerant systems especially for processes characterized by continuous and sampled (discrete) signals.

In general, almost all physical signals are continuous, for example, position and velocity of a body, speech or music picked up by a microphone, voltage or current in an electric circuit.

The sampling (instantaneous) of an analog signal or waveform is the process by which the signal is represented by a discrete set of numbers. These numbers, or samples, are equal to the signal value at well-determined instants (the sampling times). Samples must be obtained in such a way that it is possible to reconstruct the signal accurately. That is, the original waveform, defined in "continuous" time, is represented in "discrete" time by samples obtained at conveniently spaced sampling instants.

An application-oriented approach will also be done with methods that have proven their proper performance in practical applications.
