**2. Monitoring and supervision of systems**

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 the future.

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.

*Fault Detection, Diagnosis and Prognosis*

the operation of production systems [1, 2].

are a vital part of the innovative and modern systems of automatic management of

managing their severities, while ensuring an increase in overall security.

corrects the problem, preventive maintenance prevents the problem.

process of issuing the report basically comprises four stages:

• Identification of the failure modes that are occurring;

remove them before the severity of a fault itself increases [4].

• Fault location;

• Evaluation of its extension;

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

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

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

• Estimation of the remaining life of the equipment or component in question.

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

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

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

**128**

eliminate them.

These changes are symptoms of possible failures in their early state, and failure to take the necessary actions to eliminate them can lead to real failures that may compromise the performance of productive systems. The justification for the monitoring and supervision systems is to avoid these defects or failures in the 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 evaluation collected) to determine whether a device or equipment is operating normally or at risk.

The content presented in this chapter is focused primarily on the areas of system monitoring and supervision. We have shown the changes that can be made in these two areas of observation and analysis of the behavior of the parameters of a system during its operation, to make them more efficient in solving problems of production systems. The fundamental objective of this information is to integrate these two areas into one set only through the use of the Smart System technique, allowing its unified application in real time in decision making, in any area of a production system [2].

The Smart System technique will make productive systems economically efficient by improving their performance, quality, reliability of supply, operational flexibility, safety, etc.

#### **2.1 Fault diagnosis monitoring systems**

It should be noted that the selection, implementation, operation and maintenance of a system for automatic fault diagnosis is a complex task [2, 7]. You must ensure that the result provided by this system is within the programmed specifications. 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.

The concept of predictive maintenance is directly linked to the monitoring of the condition (state) of one or more equipment. O monitoring as such is a basic tool for the implementation of predictive maintenance strategies. Monitoring can be classified from the point of view of the type of sensor installation (permanent or mobile), or be classified by the data acquisition strategy "continuous/on-line" or "periodic/off-line".

"Continuous/on-line" monitoring systems often work in an integrated way with the Supervisory and Control Systems, or "Supervisory Systems" of the production systems, both of which have individual requirements for data acquisition and functions totally different from one another. The integration of these two systems allows for the "continuous" acquisition of operating data and the variables of slow variation (temperatures, levels, position values, static pressures, etc.) normally available in these systems.

Automatic Diagnostic Systems - ADS are the next step to pure and simple monitoring. These more advanced systems receive information from the monitoring system and, through the use of intelligent software, can manage" Knowledge Bank", where information obtained from various physical parameters is crossed and integrated, from where a result that is closer to what one really wants: an effective aid to decision-making.

#### **2.2 Main features of automatic fault diagnostic systems**

Automatic processing systems are integrated by computer programs, focused on the technique of artificial intelligence, and are responsible for automatically processing all information from the monitoring systems. The main objective of the integration of these systems in the operation of a productive system is the automatic detection of incipient faults and their main characteristics, that is, faults that are in

**131**

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

the initial phase of their formation (early faults), their identification, location and

The main characteristic of ADSs is that they can handle large amounts of data generated by Monitoring Systems in a systematic, frequent and automatic way, and optimize the process of data storage during long periods of operation (months or years). Another attribute of the ADSs is their intrinsic characteristic, that is, throughout the time of use, each time less need of the interference of the user. Another important feature of ADSs is their adequacy as a Knowledge Management

The characteristic limitation of this type of system, as well as of any type of Monitoring System traditionally used, is presented when dealing with faults of instant or catastrophic evolution. For this, "Protection Systems", with fixed and well-established alarm limits, should be considered as the main option. The principles of operation, as well as the necessary technical characteristics, relating to the acquisition, communication and processing of data from each of these systems are

Basically, ADSs have the function of reporting the occurrence of failures when they are still in their infancy, while the Protection Systems must act at the moment

The technological development in the systems of monitoring and supervision will allow the structuring and optimized evolution of the areas of automated detection and diagnosis, this being the next step to the pure and simple monitoring. These systems receive information from the monitoring system, consisting essentially of sensors and through the use of the technique of intelligent systems and expert systems, a "knowledge bank" is managed or also called the knowledge base for decision making. The evaluation of the information provided by the monitoring and supervision system will allow to detect and locate a problem and diagnose its root cause, simultaneously, it will be possible to select the best action to mitigate changes in the behavior of the parameters of interest and eliminate the cause that produces them. Finally, the system itself will decide whether to take this action online or offline,

Another important characteristic that is considered in the design of these systems is their intrinsic characteristic that, throughout the time of use, they are less and less required to interfere with the user. That is, while in the case of traditional monitoring systems, the accumulation of stored, non-processed data by the user is a natural consequence of the monitoring process itself, and the effort to treat such data never diminishes over time. In the ADSs the manual work involved in the processing of information is decreasing over time. This is due to the fact that there are tools and mechanisms of retention and improvement of the knowledge registered in these systems. Thus, by using knowledge management tools, maintenance team members can track, correct, insert, retrieve, and refine existing content in their Knowledge Bank (expert systems). In this way, it can be said that the joint monitoring and supervision system has become intelligent and consequently autonomous to operate a production system in

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

estimation of the degree of severity.

tool in predictive maintenance [8, 9].

fundamentally different and should not be confused [10].

depending on the severity and robustness of the problem.

an efficient way from a technical as well as an economic point of view.

improve the efficiency of production systems.

**2.3 Structuring of monitoring and supervision systems in an intelligent system**

In this section, a description of the methodology used for the conversion of a monitoring and supervision system in an intelligent system that allows the detection, localization and diagnosis of failures is made possible to take the most appropriate actions to eliminate them and to seek their causes to avoid them. This will

an unacceptable operating situation occurs.

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

the initial phase of their formation (early faults), their identification, location and estimation of the degree of severity.

The main characteristic of ADSs is that they can handle large amounts of data generated by Monitoring Systems in a systematic, frequent and automatic way, and optimize the process of data storage during long periods of operation (months or years). Another attribute of the ADSs is their intrinsic characteristic, that is, throughout the time of use, each time less need of the interference of the user. Another important feature of ADSs is their adequacy as a Knowledge Management tool in predictive maintenance [8, 9].

The characteristic limitation of this type of system, as well as of any type of Monitoring System traditionally used, is presented when dealing with faults of instant or catastrophic evolution. For this, "Protection Systems", with fixed and well-established alarm limits, should be considered as the main option. The principles of operation, as well as the necessary technical characteristics, relating to the acquisition, communication and processing of data from each of these systems are fundamentally different and should not be confused [10].

Basically, ADSs have the function of reporting the occurrence of failures when they are still in their infancy, while the Protection Systems must act at the moment an unacceptable operating situation occurs.

The technological development in the systems of monitoring and supervision will allow the structuring and optimized evolution of the areas of automated detection and diagnosis, this being the next step to the pure and simple monitoring. These systems receive information from the monitoring system, consisting essentially of sensors and through the use of the technique of intelligent systems and expert systems, a "knowledge bank" is managed or also called the knowledge base for decision making.

The evaluation of the information provided by the monitoring and supervision system will allow to detect and locate a problem and diagnose its root cause, simultaneously, it will be possible to select the best action to mitigate changes in the behavior of the parameters of interest and eliminate the cause that produces them. Finally, the system itself will decide whether to take this action online or offline, depending on the severity and robustness of the problem.

Another important characteristic that is considered in the design of these systems is their intrinsic characteristic that, throughout the time of use, they are less and less required to interfere with the user. That is, while in the case of traditional monitoring systems, the accumulation of stored, non-processed data by the user is a natural consequence of the monitoring process itself, and the effort to treat such data never diminishes over time. In the ADSs the manual work involved in the processing of information is decreasing over time. This is due to the fact that there are tools and mechanisms of retention and improvement of the knowledge registered in these systems. Thus, by using knowledge management tools, maintenance team members can track, correct, insert, retrieve, and refine existing content in their Knowledge Bank (expert systems).

In this way, it can be said that the joint monitoring and supervision system has become intelligent and consequently autonomous to operate a production system in an efficient way from a technical as well as an economic point of view.

#### **2.3 Structuring of monitoring and supervision systems in an intelligent system**

In this section, a description of the methodology used for the conversion of a monitoring and supervision system in an intelligent system that allows the detection, localization and diagnosis of failures is made possible to take the most appropriate actions to eliminate them and to seek their causes to avoid them. This will improve the efficiency of production systems.

*Fault Detection, Diagnosis and Prognosis*

flexibility, safety, etc.

"periodic/off-line".

in these systems.

aid to decision-making.

**2.1 Fault diagnosis monitoring systems**

order to maximize the success in executing each of them.

**2.2 Main features of automatic fault diagnostic systems**

These changes are symptoms of possible failures in their early state, and failure to take the necessary actions to eliminate them can lead to real failures that may compromise the performance of productive systems. The justification for the monitoring and supervision systems is to avoid these defects or failures in the 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 evaluation collected) to determine whether a device or equipment is operating normally or at risk. The content presented in this chapter is focused primarily on the areas of system monitoring and supervision. We have shown the changes that can be made in these two areas of observation and analysis of the behavior of the parameters of a system during its operation, to make them more efficient in solving problems of production systems. The fundamental objective of this information is to integrate these two areas into one set only through the use of the Smart System technique, allowing its unified application in real time in decision making, in any area of a production system [2]. The Smart System technique will make productive systems economically efficient by improving their performance, quality, reliability of supply, operational

It should be noted that the selection, implementation, operation and maintenance of a system for automatic fault diagnosis is a complex task [2, 7]. You must ensure that the result provided by this system is within the programmed specifications. For this, it is necessary to use appropriate tools and strategies, in each step, in

The concept of predictive maintenance is directly linked to the monitoring of the condition (state) of one or more equipment. O monitoring as such is a basic tool for the implementation of predictive maintenance strategies. Monitoring can be classified from the point of view of the type of sensor installation (permanent or mobile), or be classified by the data acquisition strategy "continuous/on-line" or

"Continuous/on-line" monitoring systems often work in an integrated way with the Supervisory and Control Systems, or "Supervisory Systems" of the production systems, both of which have individual requirements for data acquisition and functions totally different from one another. The integration of these two systems allows for the "continuous" acquisition of operating data and the variables of slow variation (temperatures, levels, position values, static pressures, etc.) normally available

Automatic Diagnostic Systems - ADS are the next step to pure and simple monitoring. These more advanced systems receive information from the monitoring system and, through the use of intelligent software, can manage" Knowledge Bank", where information obtained from various physical parameters is crossed and integrated, from where a result that is closer to what one really wants: an effective

Automatic processing systems are integrated by computer programs, focused on the technique of artificial intelligence, and are responsible for automatically processing all information from the monitoring systems. The main objective of the integration of these systems in the operation of a productive system is the automatic detection of incipient faults and their main characteristics, that is, faults that are in

**130**

Smart System or Smart Grid in general terms is the application of information technologies in production systems, integrated with communication systems and with an automated network infrastructure. This technique requires the installation of sensors in all the fundamental equipment of the production systems, structuring a reliable two-way communication system with wide coverage with the various devices and automation of the physical assets.

The current sensors have chips that detect information about the behavior of the parameters of certain equipment. These devices collect the information and those with changes are sent to an operation center through a communication system where they are analyzed to determine what is significant.

This process must occur in real time and online mode and in the presence of significant information, a centralized analysis system (specialized software) will evaluate them and determine the changes that have occurred and what should be done to improve the performance of a given parameter.

**133**

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

In **Figure 1**, a block diagram is presented where the sequential structure of an intelligent monitoring and supervision system is described at a macro level. This configuration is a technological innovation in the area of intelligent automation. Its implementation is done through computational software of reference that will help in the process of evaluation, detection, location, diagnosis and application of the

An overview of each of the steps that will make up the intelligent monitoring and supervision systems (see flowchart in **Figure 1**) will be presented, highlighting the methodologies and techniques that will be used in each one of them in order to reach the required efficiency level which allows solving the various problems that arise in the equipment used in the production processes. This efficiency will be measured by the degree of automatism in real time and the autonomy in decision making in the presence of a certain disturbance. This will indicate a fully intelligent system that will safeguard the integrity and security of a production system, avoid-

To monitor is to observe, analyze and be aware of possible signs that something is not normal. In information technology, "not normal" can indicate unavailability

In this phase the observation of changes or changes in the modules of the parameters of transcendence in time is realized. These changes must be recorded within a data collection system called the Database, which will allow us to construct a history of the behavior in time of a given variable or parameter according to a reference

This process is carried out only through a robust system of sensors, installed at strategic points of equipment or system, allowing its observability continuous

a.*Digital recorders*—perform digital recording of all information from the sensors. It is through these devices that the history of the behavior of a parameter or variable in time is constructed. This information is usually stored in the

b.*Remote digital sensors (threshold)*—transducer is the name given to a sensor or actuator, which in turn are devices for detection and actuation in a given process.

With the advent of microcontrollers and microprocessors and the great availability of tools and resources for the processing of digital systems, it was possible to

The intelligent transducer, which is the integration of: (a) an analog or digital

In **Table 1**, the sensors that are part of the two types of sensors existent for the

A smart transducer transforms the sensor's raw signals into a standardized digital representation, transmitting this digital signal to its users through a stan-

c.*Oscillography*—aims to enable the post-event analysis of disturbances, different from the protection systems that must act in real time in response to

sensor or an actuator, (b) a processing unit, and (c) a network interface.

of one or more parts of a system or simply change a parameter of a device.

Monitoring is carried out using the following methodologies:

introduce a high computing capacity to the transducers.

realization of the monitoring process are described.

dardized digital communication protocol.

most appropriate actions in the elimination of a problem or failure.

ing collapses and economic and technical damages.

level or threshold of behavior [11, 12].

*2.3.1 Monitoring*

in time [1].

binary system.

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

**Figure 1.**

*Flowchart of intelligent monitoring and supervision system.*

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

In **Figure 1**, a block diagram is presented where the sequential structure of an intelligent monitoring and supervision system is described at a macro level. This configuration is a technological innovation in the area of intelligent automation. Its implementation is done through computational software of reference that will help in the process of evaluation, detection, location, diagnosis and application of the most appropriate actions in the elimination of a problem or failure.

An overview of each of the steps that will make up the intelligent monitoring and supervision systems (see flowchart in **Figure 1**) will be presented, highlighting the methodologies and techniques that will be used in each one of them in order to reach the required efficiency level which allows solving the various problems that arise in the equipment used in the production processes. This efficiency will be measured by the degree of automatism in real time and the autonomy in decision making in the presence of a certain disturbance. This will indicate a fully intelligent system that will safeguard the integrity and security of a production system, avoiding collapses and economic and technical damages.

## *2.3.1 Monitoring*

*Fault Detection, Diagnosis and Prognosis*

devices and automation of the physical assets.

where they are analyzed to determine what is significant.

done to improve the performance of a given parameter.

Smart System or Smart Grid in general terms is the application of information technologies in production systems, integrated with communication systems and with an automated network infrastructure. This technique requires the installation of sensors in all the fundamental equipment of the production systems, structuring a reliable two-way communication system with wide coverage with the various

The current sensors have chips that detect information about the behavior of the parameters of certain equipment. These devices collect the information and those with changes are sent to an operation center through a communication system

This process must occur in real time and online mode and in the presence of significant information, a centralized analysis system (specialized software) will evaluate them and determine the changes that have occurred and what should be

**132**

**Figure 1.**

*Flowchart of intelligent monitoring and supervision system.*

To monitor is to observe, analyze and be aware of possible signs that something is not normal. In information technology, "not normal" can indicate unavailability of one or more parts of a system or simply change a parameter of a device.

In this phase the observation of changes or changes in the modules of the parameters of transcendence in time is realized. These changes must be recorded within a data collection system called the Database, which will allow us to construct a history of the behavior in time of a given variable or parameter according to a reference level or threshold of behavior [11, 12].

This process is carried out only through a robust system of sensors, installed at strategic points of equipment or system, allowing its observability continuous in time [1].

Monitoring is carried out using the following methodologies:


With the advent of microcontrollers and microprocessors and the great availability of tools and resources for the processing of digital systems, it was possible to introduce a high computing capacity to the transducers.

The intelligent transducer, which is the integration of: (a) an analog or digital sensor or an actuator, (b) a processing unit, and (c) a network interface.

A smart transducer transforms the sensor's raw signals into a standardized digital representation, transmitting this digital signal to its users through a standardized digital communication protocol.

In **Table 1**, the sensors that are part of the two types of sensors existent for the realization of the monitoring process are described.

c.*Oscillography*—aims to enable the post-event analysis of disturbances, different from the protection systems that must act in real time in response to


## **Table 1.**

*Types of sensors.*

disturbances. In fact, oscillography is a complementary tool to the protection systems, as it allows the specialist in the analysis of disturbances to verify the adjustments of a given protection, as well as any defects that may arise.

A very useful calculation performed by specialists from the oscillograms is to determine the distance at which a disturbance occurred. In this case, the specialist informs the maintenance team in which region of the transmission line it must act in order to repair the damage caused by the disturbance, making its work easier and more efficient. In addition, the expert performs other procedures, such as the phasor analysis to verify the balance between the phases and the harmonic analysis to observe the intensity of the harmonics present in the signal.

The digitalization of the oscillography signals motivated the growth of the number of computational tools developed to aid in the analysis of perturbations, also allowing the development of sophisticated signal processing tools and intelligent processing systems.

Nowadays the use of oscillography has become quite frequent for the recording of events in production systems (electrical systems, mechanical systems, etc.), since it is possible to observe the development sequence of an event and the interaction between the elements of the system that are part of the event. This implies the progressive growth of the number of oscillography files.

Thus, the need arises to study and develop compression methods with the purpose of reducing the space needed to store these files and make better use of the resources. It is proposed the use of a compression method by synthesis of oscillography files, using redundant adaptive decompositions, which provide a coherent representation with the phenomena present in the recorded signals. These decompositions were based on the technique of Matching Pursuits (MP).

Remote and real time monitoring allows observation of data related to operating conditions, mechanical parameters (fuel, temperature, engine speed, oil pressure and level, vibration, etc.), electrical parameters (currents, powers, voltages, oscillations, etc.), hydraulic parameters (flow, cavitations, water hammer, etc.) and operating hours.

#### *2.3.2 Supervision*

It is a process that performs the analysis of collected data for detection of unwanted or non-permitted states. It is searched if a parameter is within the permitted limits or if there are unusual variations.

The supervision area receives information from the Monitoring System and, through the use of intelligent software, a "Knowledge Bank" must be managed, where information obtained from various physical parameters is crossed and

**135**

b.Kalman filter

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

integrated, from which a result that is closer to what one really wants: an effective

The supervisory system automatically processes the information collected by the monitoring system through internal routines using intelligent techniques (Computational Intelligence). The objective is the automatic detection of incipient faults, that is, early detection of faults, their identification, location and estimation

2.Diagnosis of the changes present in these collected data - the system will inform if they are faults in their precocious state or catastrophic failures and

3.Elimination of failures and their root cause. This will prevent damage and collapse in a production process. Here will be decided the actions that must be taken and depending on the severity of the failure its execution will be online or offline.

At this stage, the detection of variations or changes in the normal behavior of a parameter is carried out. For this, 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

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. This part of the evaluation uses only stored (digital) data

An evaluation of the recorded oscillograms is also performed, interpreting all

To perform the database evaluation process with the historical behavior of the parameters of interest, there are tools or methodologies that allow the execution of this process in an optimized and efficient manner. These methodologies are as

At this stage of the research the most used statistical method is least squares. *Applies mainly to processes with linear characteristics*. The probabilistic methods most commonly used to determine the probabilities of states and the probability density function of an equipment or system are: (a) state space method or Markovian process that describes states and possible transitions between them, (b) Monte Carlo simulation performs several computational simulations of a process for a certain period, ending the simulations procedure, estimating the desired indices as the probability of a failure to occur, its frequency and the duration of the failure.

The Kalman filter produces estimates of the actual values of measured quantities and associated values, predicting a value, estimating the uncertainty of the

Within the area of supervision three very significant and decisive procedures are

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

aid to decision-making.

*2.3.2.1 Evaluation*

of the degree of severity [4, 13].

performed in solving the problems of a productive system:

their root cause. It will also inform your location;

1.Evaluation of the information collected;

abnormality or simply it is an isolated eventuality.

that reaches a threshold value (reference value).

a.Statistical and Probabilistic Techniques

recorded graphs related to the behavior of a given parameter.

follows and depend greatly on the type of signal being monitored:

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

integrated, from which a result that is closer to what one really wants: an effective aid to decision-making.

The supervisory system automatically processes the information collected by the monitoring system through internal routines using intelligent techniques (Computational Intelligence). The objective is the automatic detection of incipient faults, that is, early detection of faults, their identification, location and estimation of the degree of severity [4, 13].

Within the area of supervision three very significant and decisive procedures are performed in solving the problems of a productive system:


#### *2.3.2.1 Evaluation*

*Fault Detection, Diagnosis and Prognosis*

Electromagnetic Hall effect

**Table 1.** *Types of sensors.*

> disturbances. In fact, oscillography is a complementary tool to the protection systems, as it allows the specialist in the analysis of disturbances to verify the adjustments of a given protection, as well as any defects that may arise.

A very useful calculation performed by specialists from the oscillograms is to determine the distance at which a disturbance occurred. In this case, the specialist informs the maintenance team in which region of the transmission line it must act in order to repair the damage caused by the disturbance, making its work easier and more efficient. In addition, the expert performs other procedures, such as the phasor analysis to verify the balance between the phases and the harmonic analysis

The digitalization of the oscillography signals motivated the growth of the number of computational tools developed to aid in the analysis of perturbations, also allowing the development of sophisticated signal processing tools and intel-

Thus, the need arises to study and develop compression methods with the purpose of reducing the space needed to store these files and make better use of the resources. It is proposed the use of a compression method by synthesis of oscillography files, using redundant adaptive decompositions, which provide a coherent representation with the phenomena present in the recorded signals. These decom-

Remote and real time monitoring allows observation of data related to operating

conditions, mechanical parameters (fuel, temperature, engine speed, oil pressure and level, vibration, etc.), electrical parameters (currents, powers, voltages, oscillations, etc.), hydraulic parameters (flow, cavitations, water hammer, etc.) and

It is a process that performs the analysis of collected data for detection of unwanted or non-permitted states. It is searched if a parameter is within the

The supervision area receives information from the Monitoring System and, through the use of intelligent software, a "Knowledge Bank" must be managed, where information obtained from various physical parameters is crossed and

Nowadays the use of oscillography has become quite frequent for the recording of events in production systems (electrical systems, mechanical systems, etc.), since it is possible to observe the development sequence of an event and the interaction between the elements of the system that are part of the event. This implies the

to observe the intensity of the harmonics present in the signal.

**Active sensors Passive sensors** Thermoelectric Resistive Piezoelectric Capacitive Pyro electric Inductive Photovoltaic Resonant

progressive growth of the number of oscillography files.

permitted limits or if there are unusual variations.

positions were based on the technique of Matching Pursuits (MP).

ligent processing systems.

**134**

operating hours.

*2.3.2 Supervision*

At this stage, the detection of variations or changes in the normal behavior of a parameter is carried out. For this, 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 simply 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. This part of the evaluation uses only stored (digital) data that reaches a threshold value (reference value).

An evaluation of the recorded oscillograms is also performed, interpreting all recorded graphs related to the behavior of a given parameter.

To perform the database evaluation process with the historical behavior of the parameters of interest, there are tools or methodologies that allow the execution of this process in an optimized and efficient manner. These methodologies are as follows and depend greatly on the type of signal being monitored:

#### a.Statistical and Probabilistic Techniques

At this stage of the research the most used statistical method is least squares. *Applies mainly to processes with linear characteristics*. The probabilistic methods most commonly used to determine the probabilities of states and the probability density function of an equipment or system are: (a) state space method or Markovian process that describes states and possible transitions between them, (b) Monte Carlo simulation performs several computational simulations of a process for a certain period, ending the simulations procedure, estimating the desired indices as the probability of a failure to occur, its frequency and the duration of the failure.

#### b.Kalman filter

The Kalman filter produces estimates of the actual values of measured quantities and associated values, predicting a value, estimating the uncertainty of the

predicted value, and calculating a weighted average between the predicted value and the measured value. The highest weight is given to the least uncertainty value. The estimates generated by the method tend to be closer to the actual values than the original measurements, since the weighted average presents a better estimate of uncertainty than both values used in its calculation. From a theoretical point of view, the Kalman filter is an algorithm for efficiently making accurate inferences about a linear dynamic system, which is a Bayesian model similar to a Markov hidden model, but where the state space of the variables is not observed is continuous and all observed and unobserved variables have normal distribution (or often multivariate normal distribution).
