CA2666894A1 - System for condition-based maintenance of complex equipment and structures - Google Patents
System for condition-based maintenance of complex equipment and structures Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q90/00—Systems or methods specially adapted for administrative, commercial, financial, managerial or supervisory purposes, not involving significant data processing
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- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
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Abstract
The integrated diagnostic system provides an integrated methodology for generator diagnostics using the results from on-site measurement tools, which help the utility to make the transition from time-based maintenance to condition-based maintenance.
The system makes use of information technology (Internet) to provide a new, modern and efficient way to produce a continuous classification of the condition of all generators of a fleet along with individual diagnostics for any unit at any time. The system computes actual measurements provided by plant personnel who transfer data files to a centralized server. The application calculates simple condition indexes from each one of on-line/off-line diagnostic tools and from visual inspections, and aggregates the results into a comprehensive global diagnostic for the selected generator. Plant and generator selection is done via a user-friendly interface displaying a simple rating of the results for every tool. The algorithm underlying the system generates a global diagnostic for any combination of tools, regardless of their number and selection. The level of confidence of the diagnostic increases with the number of tools used for the diagnostic. In addition to the simplified integrated condition index values of all generators and the individual index for each tool, specialists can access and display the complete data for every measurement series.
The tools are selected based on their ability to characterize specific complementary aspects of the generator. Since the system is developed with an expandable modular approach, it is possible to add new diagnostic tools without affecting the logic of the system. The ready availability of centralized, simplified information makes it possible for generator specialists and managers alike to assess the condition of any generator in a few minutes. Technical and management staff can work together with common information and in real time to optimize maintenance intervention on generators showing degradation. Thus, it is possible to plan any corrective action more effectively or request additional testing when doubts remain about active degradation mechanisms. At the same time, efforts in diagnosis and maintenance can be optimized by reducing the number of measurement campaigns for the vast majority of generators that are in good condition as revealed by their condition indexes.
The system makes use of information technology (Internet) to provide a new, modern and efficient way to produce a continuous classification of the condition of all generators of a fleet along with individual diagnostics for any unit at any time. The system computes actual measurements provided by plant personnel who transfer data files to a centralized server. The application calculates simple condition indexes from each one of on-line/off-line diagnostic tools and from visual inspections, and aggregates the results into a comprehensive global diagnostic for the selected generator. Plant and generator selection is done via a user-friendly interface displaying a simple rating of the results for every tool. The algorithm underlying the system generates a global diagnostic for any combination of tools, regardless of their number and selection. The level of confidence of the diagnostic increases with the number of tools used for the diagnostic. In addition to the simplified integrated condition index values of all generators and the individual index for each tool, specialists can access and display the complete data for every measurement series.
The tools are selected based on their ability to characterize specific complementary aspects of the generator. Since the system is developed with an expandable modular approach, it is possible to add new diagnostic tools without affecting the logic of the system. The ready availability of centralized, simplified information makes it possible for generator specialists and managers alike to assess the condition of any generator in a few minutes. Technical and management staff can work together with common information and in real time to optimize maintenance intervention on generators showing degradation. Thus, it is possible to plan any corrective action more effectively or request additional testing when doubts remain about active degradation mechanisms. At the same time, efforts in diagnosis and maintenance can be optimized by reducing the number of measurement campaigns for the vast majority of generators that are in good condition as revealed by their condition indexes.
Description
SYSTEM FOR CONDITION-BASED MAINTENANCE OF COMPLEX EQUIPMENT
AND STRUCTURES
Since deregulation of the electricity market in America's Northeast over the last ten years, the demands on existing generators have introduced new operating rules:
multiple daily starts and stops, spin-no-load availability and machines pushed to their limits when market prices are attractively high. In order to benefit from this new reality, the short-term advantages of pushing generators should not be outweighed by a reduction in the reliability or in the expected life of the equipment. The traditional model of time-based maintenance that was used when generators were employed for base load production is not well suited to the new strenuous conditions because degradation is expected to accelerate unless the mean time between maintenance interventions is reduced. Clearly, the resulting increase in maintenance costs is not a desirable solution. To keep reliability at an acceptable level effectively, the best option is to migrate from time-based maintenance to condition-based maintenance (CBM).
Unfortunately, this is easier said than done, because it requires a significant change in practice, in the sense that knowing the condition of a generator calls for a variety of results from different constantly available measurement tools. This information then needs to be logged, analyzed and displayed in order to have an appreciation of not only each generator, but also of a whole plant or even the entire fleet of generators.
Over the last decade, several diagnostic platforms have been proposed as integrated systems or expert systems, stressing the need for improvement in maintenance practices to make an efficient transition to CBM. The proposed approaches have their strengths, but none is entirely satisfying. There are many detailed case studies analyzing results from a single tool such as PD measurements or air gap monitoring systems. But one disappointing finding is that, over time, none of the systems developed during the 1990s left a trail of successful case studies combining measurement results and analysis from multiple tools. Moreover, some diagnostic applications disappeared entirely shortly after being introduced. One of the conclusions drawn from this is that a good idea is not always viable. To improve the chances of long-term success of any system, its use and maintenance should be as simple as possible, because once the developers are out of the loop, if the end-user does not grasp the potential of the system to make his/her work easier, he/she will not use it. The choice of using a Web-based application in accordance with the invention addresses both these issues. First, direct access from computers anywhere within the utility will provide quick, straightforward information via a user-friendly interface.
Second, any new data file, logged intervention or change in the application is now centralized on a main server and the information is constantly updated and always the same for everyone.
The need for such a system is probably even greater nowadays, as many utilities are facing the problem of losing experts with years of experience, who most of the time have gained sufficient background knowledge about their plants to keep equipment failure risk to a minimum. When those experts retire, most of their knowledge will be lost. Without a way to capture this wealth of information and face the change from traditional base-load operation of generators to a load-cycling operation mode imposed by open-market rules, reduction of the reliability of generators belongs to the realm of the unknown. With the integrated diagnostic system according to the invention, it will be easier to determine the rate of degradation of any failure mode and keep track of the condition of all machines. The system is also a perfect training bench for new engineers and technicians because it compiles what experts have gathered during their careers. Once the personnel have learnt to use the diagnostic system, they can use it wherever they go because it is accessible from anywhere in the utility. In this way, the system alleviates the problems raised by personnel rotation. Once an employee gets to a new position, he/she can readily access all pertinent information about the condition of the generators in the new plant and should be better able to manage their maintenance.
Since optimization of the maintenance program is about cost-effectiveness, there is no point in building a diagnostic system more expensive than a time-based maintenance program. One object of the invention is therefore to establish a diagnostic strategy that limits the number of measurement points/tools used for generators in good condition and pay closer attention to those showing signs of deterioration. As will be presented below, this is possible with a judicious combination of on-line/off-line measurement tools.
In addition to the diagnostic strategy, another object is to design a global approach that attributes roles with identified tasks to every specialist and technician involved without increasing their workload. This can be done by reorganizing part of the work they are already doing.
One aspect of the invention is the development of the system using Web-based technology and providing a user-friendly system that maintenance personnel can use to transfer new measurement data and consult past results. The system is more than just a centralized data bank of raw data: the core of the system includes the logic to analyze, aggregate and display condition indexes for each generator in a simple comprehensive form. Automation of this step provides large payback in data-processing time, because one expert alone cannot measures, analyzes, classifies and provides an overall picture of a large number of generators. Moreover, to keep all this information updated is an impossible task for any individual or even for an entire department unless they rely on an automated diagnostic system such as the system according to the invention. Furthermore, it is almost impossible to remember every maintenance action and to correlate its impact on measurement results, yet this information is also a part of the equation that the maintenance specialist needs to consider in his/her diagnostic. Without a global centralized system, it is very laborious just to obtain all the information on any one generator whenever necessary.
By converting part of the knowledge of diagnostic specialists into the diagnostic system, at least a first classification of generators can be done, leaving the few specialists still around to work on a more detailed diagnostic of those generators requiring special attention. Typically, considering an annual generator failure rate around 1.5%, it can be assumed that the large majority of the units do not require a comprehensive diagnostic through a technical audit program. Since condition based maintenance relies on the interpretation of raw measurements, some of the specialists' knowledge for each measurement tool used in the diagnostic is encoded.
In addition, by fixing acceptable limits for each tool, interpretation accessible to anyone is achieved, whereas in the past it has often been only within the reach of specialists. Another difficulty overcome by the invention is to combine the results from different tests in order to generate a reliable diagnostic for any generator, regardless of the number and combination of diagnostic tools used. Finally, the system is simple and easy to use for anyone, even those with little knowledge about specific diagnostic results and their interpretation. At the same time, it provides detailed results from every diagnostic tool so that a specialist can go back to the raw data and perform a more detailed diagnostic than that made automatically.
The diagnostic can be implemented for slow degradation processes evolving over years if not decades. For such degradation mechanisms, which concern most failure modes, there is no need to monitor parameters such as partial discharge (PD) activity every minute. The system can rely on periodic measurements performed every year or more, depending on the type of diagnostic tool.
The integrated diagnostic system provides two levels of information: general easy-to-understand information with overall generator classification and detailed diagnostic of every single unit. The combination of the two levels improves the diagnosis of problematic generators and help specialists to decide where and when a more detailed diagnostic may be necessary. At the same time, most generators observed to be in good condition in the system continue to operate with minimal diagnostic efforts. Thus, as maintenance budgets are optimized, the overall reliability of the fleet increases over time.
Perfect diagnosis of a generator is difficult or almost impossible because it requires many measurements coming from different combinations of diagnostic tools. In addition, it should be pointed out that no single tool alone can provide an overall view of all components. Meanwhile, detailed evaluation is not necessary for generators in good condition, which represent the majority of the fleet. However, detailed diagnostics could be necessary for specific generators to determine their exact condition and see if they are suitable for operation or if they require corrective maintenance. In order to optimize the maintenance and diagnostic efforts with the increasing need for generation, a three-level diagnostic strategy is proposed, as illustrated in Figure 1. It suggests that on-line diagnostic tools should be deployed to obtain an initial diagnostic of all generators, while maximizing their availability.
Several tools can be contemplated for this Level-1 diagnostic, and those used should be selected for their capability to reveal a wide variety of problems. This first line of diagnostics may eventually be used for triggering the next two levels. The diagnostic tools of levels 1, 2 and 3 can therefore be selected so as to be complementary. An illustrative first selection for Level-1 can be PD (partial discharge) and ozone measurements, but other tools can also be used, e.g. air gap monitor or temperature measurements. Since large generators are often instrumented with PD couplers, PD
measurement can be a logical choice. For smaller machines not equipped with PD
couplers, periodic ozone measurements can be used as the Level-1 diagnostic.
AND STRUCTURES
Since deregulation of the electricity market in America's Northeast over the last ten years, the demands on existing generators have introduced new operating rules:
multiple daily starts and stops, spin-no-load availability and machines pushed to their limits when market prices are attractively high. In order to benefit from this new reality, the short-term advantages of pushing generators should not be outweighed by a reduction in the reliability or in the expected life of the equipment. The traditional model of time-based maintenance that was used when generators were employed for base load production is not well suited to the new strenuous conditions because degradation is expected to accelerate unless the mean time between maintenance interventions is reduced. Clearly, the resulting increase in maintenance costs is not a desirable solution. To keep reliability at an acceptable level effectively, the best option is to migrate from time-based maintenance to condition-based maintenance (CBM).
Unfortunately, this is easier said than done, because it requires a significant change in practice, in the sense that knowing the condition of a generator calls for a variety of results from different constantly available measurement tools. This information then needs to be logged, analyzed and displayed in order to have an appreciation of not only each generator, but also of a whole plant or even the entire fleet of generators.
Over the last decade, several diagnostic platforms have been proposed as integrated systems or expert systems, stressing the need for improvement in maintenance practices to make an efficient transition to CBM. The proposed approaches have their strengths, but none is entirely satisfying. There are many detailed case studies analyzing results from a single tool such as PD measurements or air gap monitoring systems. But one disappointing finding is that, over time, none of the systems developed during the 1990s left a trail of successful case studies combining measurement results and analysis from multiple tools. Moreover, some diagnostic applications disappeared entirely shortly after being introduced. One of the conclusions drawn from this is that a good idea is not always viable. To improve the chances of long-term success of any system, its use and maintenance should be as simple as possible, because once the developers are out of the loop, if the end-user does not grasp the potential of the system to make his/her work easier, he/she will not use it. The choice of using a Web-based application in accordance with the invention addresses both these issues. First, direct access from computers anywhere within the utility will provide quick, straightforward information via a user-friendly interface.
Second, any new data file, logged intervention or change in the application is now centralized on a main server and the information is constantly updated and always the same for everyone.
The need for such a system is probably even greater nowadays, as many utilities are facing the problem of losing experts with years of experience, who most of the time have gained sufficient background knowledge about their plants to keep equipment failure risk to a minimum. When those experts retire, most of their knowledge will be lost. Without a way to capture this wealth of information and face the change from traditional base-load operation of generators to a load-cycling operation mode imposed by open-market rules, reduction of the reliability of generators belongs to the realm of the unknown. With the integrated diagnostic system according to the invention, it will be easier to determine the rate of degradation of any failure mode and keep track of the condition of all machines. The system is also a perfect training bench for new engineers and technicians because it compiles what experts have gathered during their careers. Once the personnel have learnt to use the diagnostic system, they can use it wherever they go because it is accessible from anywhere in the utility. In this way, the system alleviates the problems raised by personnel rotation. Once an employee gets to a new position, he/she can readily access all pertinent information about the condition of the generators in the new plant and should be better able to manage their maintenance.
Since optimization of the maintenance program is about cost-effectiveness, there is no point in building a diagnostic system more expensive than a time-based maintenance program. One object of the invention is therefore to establish a diagnostic strategy that limits the number of measurement points/tools used for generators in good condition and pay closer attention to those showing signs of deterioration. As will be presented below, this is possible with a judicious combination of on-line/off-line measurement tools.
In addition to the diagnostic strategy, another object is to design a global approach that attributes roles with identified tasks to every specialist and technician involved without increasing their workload. This can be done by reorganizing part of the work they are already doing.
One aspect of the invention is the development of the system using Web-based technology and providing a user-friendly system that maintenance personnel can use to transfer new measurement data and consult past results. The system is more than just a centralized data bank of raw data: the core of the system includes the logic to analyze, aggregate and display condition indexes for each generator in a simple comprehensive form. Automation of this step provides large payback in data-processing time, because one expert alone cannot measures, analyzes, classifies and provides an overall picture of a large number of generators. Moreover, to keep all this information updated is an impossible task for any individual or even for an entire department unless they rely on an automated diagnostic system such as the system according to the invention. Furthermore, it is almost impossible to remember every maintenance action and to correlate its impact on measurement results, yet this information is also a part of the equation that the maintenance specialist needs to consider in his/her diagnostic. Without a global centralized system, it is very laborious just to obtain all the information on any one generator whenever necessary.
By converting part of the knowledge of diagnostic specialists into the diagnostic system, at least a first classification of generators can be done, leaving the few specialists still around to work on a more detailed diagnostic of those generators requiring special attention. Typically, considering an annual generator failure rate around 1.5%, it can be assumed that the large majority of the units do not require a comprehensive diagnostic through a technical audit program. Since condition based maintenance relies on the interpretation of raw measurements, some of the specialists' knowledge for each measurement tool used in the diagnostic is encoded.
In addition, by fixing acceptable limits for each tool, interpretation accessible to anyone is achieved, whereas in the past it has often been only within the reach of specialists. Another difficulty overcome by the invention is to combine the results from different tests in order to generate a reliable diagnostic for any generator, regardless of the number and combination of diagnostic tools used. Finally, the system is simple and easy to use for anyone, even those with little knowledge about specific diagnostic results and their interpretation. At the same time, it provides detailed results from every diagnostic tool so that a specialist can go back to the raw data and perform a more detailed diagnostic than that made automatically.
The diagnostic can be implemented for slow degradation processes evolving over years if not decades. For such degradation mechanisms, which concern most failure modes, there is no need to monitor parameters such as partial discharge (PD) activity every minute. The system can rely on periodic measurements performed every year or more, depending on the type of diagnostic tool.
The integrated diagnostic system provides two levels of information: general easy-to-understand information with overall generator classification and detailed diagnostic of every single unit. The combination of the two levels improves the diagnosis of problematic generators and help specialists to decide where and when a more detailed diagnostic may be necessary. At the same time, most generators observed to be in good condition in the system continue to operate with minimal diagnostic efforts. Thus, as maintenance budgets are optimized, the overall reliability of the fleet increases over time.
Perfect diagnosis of a generator is difficult or almost impossible because it requires many measurements coming from different combinations of diagnostic tools. In addition, it should be pointed out that no single tool alone can provide an overall view of all components. Meanwhile, detailed evaluation is not necessary for generators in good condition, which represent the majority of the fleet. However, detailed diagnostics could be necessary for specific generators to determine their exact condition and see if they are suitable for operation or if they require corrective maintenance. In order to optimize the maintenance and diagnostic efforts with the increasing need for generation, a three-level diagnostic strategy is proposed, as illustrated in Figure 1. It suggests that on-line diagnostic tools should be deployed to obtain an initial diagnostic of all generators, while maximizing their availability.
Several tools can be contemplated for this Level-1 diagnostic, and those used should be selected for their capability to reveal a wide variety of problems. This first line of diagnostics may eventually be used for triggering the next two levels. The diagnostic tools of levels 1, 2 and 3 can therefore be selected so as to be complementary. An illustrative first selection for Level-1 can be PD (partial discharge) and ozone measurements, but other tools can also be used, e.g. air gap monitor or temperature measurements. Since large generators are often instrumented with PD couplers, PD
measurement can be a logical choice. For smaller machines not equipped with PD
couplers, periodic ozone measurements can be used as the Level-1 diagnostic.
In the normal scheme, only generators showing abnormal readings require a second-level diagnostic. This level may consist mostly of off-line diagnostic tools, which require partial dismantling of the machine or at least a minimum downtime, typically less than half a day. Level-2 consists of tools such as polarization/depolarization current measurements, DC ramp test measurements and limited visual inspection.
The selection of the tests to be performed may depend on the results of the first level.
If the second-level results confirm the potential risk stressed at Level-1 but are insufficient to ascertain and decide the course of action and the exact nature of the active failure mode, a third-level diagnostic may be necessary before putting the generator back on-line. This third level is more intrusive than the first two and should be limited to the few units where the two first levels were unable to provide a complete diagnostic. Tools and diagnostic techniques used in Level-3 can include detailed visual inspection, resistance measurements between the stator bar semi-conductive armor and ground, radial wedge tightness measurements, TVA
(Tennessee Valley Authority) probe or ELCID (Electromagnetic Core Imperfection Detection) readings and many others. The tools can be selected based on the analysis of the previous results. All of these tests/techniques require substantial dismantling of generator components such as fans, shrouds or air baffles, the removal of few poles or the entire rotor. The detailed information gained from these results is of great value but is obtained at significant cost. This is why Level-3 diagnostics should not be done on all generators, but limited to those singled out by levels I and 2 as requiring more input in order to determine the cause and the solution so that the failure risk can be reduced to an acceptable level. This does not mean that when the opportunity arises, such as a change of runner, that the three-level sequence always needs to be respected. In fact, at any time, any individual diagnostic tool or technique can be used, and the results are considered by the system, which automatically provides an overall diagnostic for the generator under evaluation regardless of the tools used.
In addition to the strategy applied in the field and presented above, the diagnostic system itself can be structured as summarized in Figure 2. The first part of the process, seen on the left-hand side of the figure, pertains to data collection. With the system according to the invention, data can be collected and processed more systematically, according to the three-level diagnostic strategy using predefined tools.
Once measurements are made, everyone can directly access the integrated diagnostic system through a simple interface designed to accept files from every tool and transfer them to a centralized database. This feature departs from traditional methods where the results were saved locally on PCs, in folders or on a shelf, to be later laboriously retrieved when it came time to perform the diagnosis of a generator while requiring specialist knowledge about all diagnostic tools in order to come up with a diagnostic. Even when all results were available, it was never a straightforward task to propose a diagnostic based on the results from several diagnostic tools because millivolts or picocoulombs from PD measurements cannot readily be compared or combined with polarization index values or with evidence of magnetic core buckling.
The selection of the tests to be performed may depend on the results of the first level.
If the second-level results confirm the potential risk stressed at Level-1 but are insufficient to ascertain and decide the course of action and the exact nature of the active failure mode, a third-level diagnostic may be necessary before putting the generator back on-line. This third level is more intrusive than the first two and should be limited to the few units where the two first levels were unable to provide a complete diagnostic. Tools and diagnostic techniques used in Level-3 can include detailed visual inspection, resistance measurements between the stator bar semi-conductive armor and ground, radial wedge tightness measurements, TVA
(Tennessee Valley Authority) probe or ELCID (Electromagnetic Core Imperfection Detection) readings and many others. The tools can be selected based on the analysis of the previous results. All of these tests/techniques require substantial dismantling of generator components such as fans, shrouds or air baffles, the removal of few poles or the entire rotor. The detailed information gained from these results is of great value but is obtained at significant cost. This is why Level-3 diagnostics should not be done on all generators, but limited to those singled out by levels I and 2 as requiring more input in order to determine the cause and the solution so that the failure risk can be reduced to an acceptable level. This does not mean that when the opportunity arises, such as a change of runner, that the three-level sequence always needs to be respected. In fact, at any time, any individual diagnostic tool or technique can be used, and the results are considered by the system, which automatically provides an overall diagnostic for the generator under evaluation regardless of the tools used.
In addition to the strategy applied in the field and presented above, the diagnostic system itself can be structured as summarized in Figure 2. The first part of the process, seen on the left-hand side of the figure, pertains to data collection. With the system according to the invention, data can be collected and processed more systematically, according to the three-level diagnostic strategy using predefined tools.
Once measurements are made, everyone can directly access the integrated diagnostic system through a simple interface designed to accept files from every tool and transfer them to a centralized database. This feature departs from traditional methods where the results were saved locally on PCs, in folders or on a shelf, to be later laboriously retrieved when it came time to perform the diagnosis of a generator while requiring specialist knowledge about all diagnostic tools in order to come up with a diagnostic. Even when all results were available, it was never a straightforward task to propose a diagnostic based on the results from several diagnostic tools because millivolts or picocoulombs from PD measurements cannot readily be compared or combined with polarization index values or with evidence of magnetic core buckling.
To simplify analysis and aggregation of results, algorithms are implemented in the system in order to determine individual condition indexes for each of the tools selected. The rating of the results can be based on a 1 to 5 scale, from best to worst, for every measurement technique. An individual index of 5 for one tool does not necessarily translate into a short-term failure risk of a generator or one of its components but, when this flag comes up, the results can be further analyzed by a maintenance specialist. For instance, a high level of contamination in the end-windings may give an individual index of 5 for the polarization current, but cleaning of the machine would most probably reduce this index to a lower acceptable value.
The generation of individual indexes is based on sets of rules reproducing the judgment of the expert in this technical field.
The equilibrium between the three diagnostic levels in Figure 1 and their ability to cover the maximum of failure modes is preferably considered in the selection of the diagnostic tools. A selection of complementary instruments leading to a global diagnostic of all major components of the generator can be considered. For example, on-line 2-D PD measurement (pulse repetition rate vs. magnitude) can be used as the first-level diagnostic. The second-level tool selected can be an instrument measuring polarization and depolarization currents (ITERG) from which the polarization index and insulation resistance can be extracted. Finally, the third-level diagnostic tool can be an instrument (BARTACT) measuring the resistance between the semi-conductive coating of stator bars and the stator core, which indicates whether the electrical contact between the coating of the bar and the core is good or not.
A modular approach makes the system easily expandable if more diagnostic tools are added later, for example:
= Level-1: Ozone measurements.
= Level-2: DC ramp test, limited visual inspection, Phase Resolved Partial Discharge (PRPD) measurements.
= Level-3: Complete visual inspection.
The PRPD measurement is an on-line measurement which can be used when the first-level diagnostic (2-D PD measurements) reveals a problem. Since the PRPD
is more complex to measure and to analyze, it can be decided to not performing it for generators with low PD activity.
Since visual inspection can be done with limited or substantial dismantling, it can be considered either a Level-2 or a Level-3 tool. The system has a Web-based interface to log any observations made during a visual inspection. Subjective information collected from visual inspection is translated into an objective condition index that can be aggregated with those of the other tools.
The system can be summarized by the four groups of functions illustrated in Figure 3:
Classification, Results, Trending and Documentation. Most of the information is accessible and easy for everyone to understand, but one section of the application is dedicated to maintenance specialists who need to access individual measurement files to push their diagnostic further and plan the next actions (measurements or corrections).
Once the user enters the system application, he/she sees a two-part window with a tree on the left-hand side showing all the plants where diagnostic results exist for at least one tool. On the right is a global classification of all generators.
This information is probably the most important for managers in charge of maximizing generation, while minimizing failure risks. Plant managers, however, are usually interested in a more limited classification related to only the units of their installation.
The system is designed to easily display all the generators or different groups of generators, as summarized in the left-hand column in Figure 3. The global classification, illustrated in Figure 4, can show the entire distribution of all of the generators at once (in the top graph, each bar represents a generator) or a more spread-out representation (bottom graph) allowing identification of specific units. In this view, a scroll bar allows the user to slide the distribution from worst (global index > 4 shown in red) to the best generator (global index < 2 shown in green). With a single click in the tree structure, it is also possible to display a partial classification such as the one in Figure 5 for the 12 units of power plant "X". In this representation, the calculated diagnostic of the generators can be seen to go from a global condition index of 4.0 down to 1.2 for generators with a similar number of operating hours and start/stop sequences.
Thus, not all generators of this plant aged exactly alike. When the time comes to replace them, the decision of the sequence in which to do so, while minimizing the failure risk, is facilitated by this classification. Other types of grouping can also be displayed to compare the degradation of generators with thermosetting or thermoplastic insulation, generators from different manufacturers, etc. Finally, similar classifications (global or partial) are also available for every single tool.
The results displayed in Figures 4 and 5 are those of the global diagnostic coming from the aggregation of all diagnostic tools used over the years for a number of generators. Aggregation algorithms implemented in the system give a valid global diagnostic regardless of the number or the set of tools used in the diagnostic.
However, similar to what a doctor would get from a medical diagnosis of a patient, the use of a few basic tools will give a first-level diagnostic (our Level-1), but with levels of confidence lower than if a large battery of tests is carried out. This is reflected by the calculation of a level of confidence that the global index corresponds to the overall condition of the generator. This confidence level is displayed by the lower bars inside each global index bar in the histograms of Figures 4 and 5. When the lower bar goes to the top of the entire bar, it means 100% confidence, whereas if the global index bar is only half-filled, it means 50% confidence. The more detailed the diagnosis (more tools and measurements over the years), the higher the confidence level. Thus, it is possible to know at a glance the global diagnostic of any generator and whether one can be confident with this information or not, stressing the need to call for additional measurements (levels 2 and 3). This need will be greater when the global index is higher. For good generators with low global indexes, only yearly measurements of Level-1 tools may be required, assuming the Level-1 diagnostic detects most of the problems affecting the generators.
The generation of individual indexes is based on sets of rules reproducing the judgment of the expert in this technical field.
The equilibrium between the three diagnostic levels in Figure 1 and their ability to cover the maximum of failure modes is preferably considered in the selection of the diagnostic tools. A selection of complementary instruments leading to a global diagnostic of all major components of the generator can be considered. For example, on-line 2-D PD measurement (pulse repetition rate vs. magnitude) can be used as the first-level diagnostic. The second-level tool selected can be an instrument measuring polarization and depolarization currents (ITERG) from which the polarization index and insulation resistance can be extracted. Finally, the third-level diagnostic tool can be an instrument (BARTACT) measuring the resistance between the semi-conductive coating of stator bars and the stator core, which indicates whether the electrical contact between the coating of the bar and the core is good or not.
A modular approach makes the system easily expandable if more diagnostic tools are added later, for example:
= Level-1: Ozone measurements.
= Level-2: DC ramp test, limited visual inspection, Phase Resolved Partial Discharge (PRPD) measurements.
= Level-3: Complete visual inspection.
The PRPD measurement is an on-line measurement which can be used when the first-level diagnostic (2-D PD measurements) reveals a problem. Since the PRPD
is more complex to measure and to analyze, it can be decided to not performing it for generators with low PD activity.
Since visual inspection can be done with limited or substantial dismantling, it can be considered either a Level-2 or a Level-3 tool. The system has a Web-based interface to log any observations made during a visual inspection. Subjective information collected from visual inspection is translated into an objective condition index that can be aggregated with those of the other tools.
The system can be summarized by the four groups of functions illustrated in Figure 3:
Classification, Results, Trending and Documentation. Most of the information is accessible and easy for everyone to understand, but one section of the application is dedicated to maintenance specialists who need to access individual measurement files to push their diagnostic further and plan the next actions (measurements or corrections).
Once the user enters the system application, he/she sees a two-part window with a tree on the left-hand side showing all the plants where diagnostic results exist for at least one tool. On the right is a global classification of all generators.
This information is probably the most important for managers in charge of maximizing generation, while minimizing failure risks. Plant managers, however, are usually interested in a more limited classification related to only the units of their installation.
The system is designed to easily display all the generators or different groups of generators, as summarized in the left-hand column in Figure 3. The global classification, illustrated in Figure 4, can show the entire distribution of all of the generators at once (in the top graph, each bar represents a generator) or a more spread-out representation (bottom graph) allowing identification of specific units. In this view, a scroll bar allows the user to slide the distribution from worst (global index > 4 shown in red) to the best generator (global index < 2 shown in green). With a single click in the tree structure, it is also possible to display a partial classification such as the one in Figure 5 for the 12 units of power plant "X". In this representation, the calculated diagnostic of the generators can be seen to go from a global condition index of 4.0 down to 1.2 for generators with a similar number of operating hours and start/stop sequences.
Thus, not all generators of this plant aged exactly alike. When the time comes to replace them, the decision of the sequence in which to do so, while minimizing the failure risk, is facilitated by this classification. Other types of grouping can also be displayed to compare the degradation of generators with thermosetting or thermoplastic insulation, generators from different manufacturers, etc. Finally, similar classifications (global or partial) are also available for every single tool.
The results displayed in Figures 4 and 5 are those of the global diagnostic coming from the aggregation of all diagnostic tools used over the years for a number of generators. Aggregation algorithms implemented in the system give a valid global diagnostic regardless of the number or the set of tools used in the diagnostic.
However, similar to what a doctor would get from a medical diagnosis of a patient, the use of a few basic tools will give a first-level diagnostic (our Level-1), but with levels of confidence lower than if a large battery of tests is carried out. This is reflected by the calculation of a level of confidence that the global index corresponds to the overall condition of the generator. This confidence level is displayed by the lower bars inside each global index bar in the histograms of Figures 4 and 5. When the lower bar goes to the top of the entire bar, it means 100% confidence, whereas if the global index bar is only half-filled, it means 50% confidence. The more detailed the diagnosis (more tools and measurements over the years), the higher the confidence level. Thus, it is possible to know at a glance the global diagnostic of any generator and whether one can be confident with this information or not, stressing the need to call for additional measurements (levels 2 and 3). This need will be greater when the global index is higher. For good generators with low global indexes, only yearly measurements of Level-1 tools may be required, assuming the Level-1 diagnostic detects most of the problems affecting the generators.
When a generator has been identified as potentially at risk and the confidence level is low, a maintenance specialist may analyze the results from the individual tools contributing to the diagnostic and recommend further measurements or corrective actions. Based on the evidence, tools from levels 2 and 3 can be selected to confirm or invalidate the existence of suspected failure modes.
One major advantage of the system according to the invention is that it offers a platform for managers and maintenance engineers to communicate using common facts (global indexes and individual tool indexes) about the condition of the units. An example of this is shown in Figure 6, where plant C is selected by clicking in the tree of the global classification interface (Figure 4) to reveal the global indexes of the four units. The right-hand side shows graphically the same information as that appearing in the tree (in Figure 6, "cote" represents the global index of the unit). In plant C, it can be seen that unit 1 is the worst with an index of 4.8 and a confidence level of 21%, whereas unit 2 is the best at 2.2 and 63%. The difference in confidence levels stems from the combination of tools used in the diagnostic, the number of measurement campaigns and the date of the last measurement (more recent measurements give a higher confidence than older ones). As time passes, the application automatically updates the confidence levels without human intervention. It is easy to see that managers do not need to be specialists in generator diagnostics to understand this information, which is accessible at any time directly from their personal computer. Up to this point, anyone, with no knowledge about interpretation of PD, ozone or DC
ramp test measurements, for instance, can access the diagnostic. If a plant manager is more concerned about a specific generator, such as unit 1 in the example of Figure 6, he can than call for help from a specialist if desired.
The next level of information is obtained by clicking directly on the unit number in the tree, which reveals the calculated individual index for each tool used in the diagnostic, as illustrated in Figure 7. Here the detailed results of unit 1 came only from PD
measurements made with the PDAH, whereas the diagnostic of unit 2 was the result of the aggregation of five different tools (BARTACT, visual inspection, ITERG, PDAH
and PRPD). This is why the confidence level of unit 1 (21 %) is lower than that of unit 2 (63%).
The system has a measurement procedure for each tool and, when it is respected, the confidence level will be higher than when it is not or only partially respected. For instance, sometimes it is not possible to carry out an entire test for lack of time or accessibility. When the data from a tool is transferred, automatic routines calculate both the index and the confidence level according to predefined rules, eliminating the subjectivity in the process.
In the example of Figure 7, it can be seen for unit 2 that the confidence level of the different diagnostic tools ranges from 97% for the PDAH down to 47% for the visual inspection. The high confidence level for PD suggests that the measurements were done in accordance with prescriptions, and that the last measurement is recent. The lower confidence for the visual inspection may indicate that too few components were inspected or that the last inspection dates back too far. Up to this level, all the information is easy to understand and allows recognizing that unit 1 is potentially in worse condition than unit 2. To confirm this, the specific PD results for unit 1 may be analyzed to understand the reason for this high index if desired. According to the detailed analysis of PD measurements, other complementary tests can be prescribed.
The detailed results for any diagnostic tool can be accessed by anyone interested. To access the results, the user only has to click on the name of the tool he/she wants to study below the number of the unit in the tree, such as the one in Figure 7.
This displays the corresponding results in the right-hand portion of the screen, as illustrated in Figure 8 for the PDAH of unit 1. The twelve graphs presented here are those of twelve couplers installed on this generator (one per parallel circuit of each of the three phase windings). Positive (darker) and negative (lighter) discharges can be compared in each histogram. Other quantities, such as amplitude, discharge rate, NQN, maximum amplitude and positive/negative ratio can all be extracted from the display. On the date of these measurements there were four data series, corresponding to the different gain settings used that day. Each series can be displayed just by clicking on the appropriate series number in the tree. Here, only one date appears but, when several measurement campaigns exist, they can all be displayed and analyzed. The detailed analysis allows determining if slot discharge, end-winding discharges or internal delamination might be present. Based on the conclusion reached, other tests can be arranged to obtain a more thorough diagnostic with an appreciation of the problem with higher level of confidence.
Sometimes, when the degradation mode identified is considered to be slow, the conclusion may be to wait one more year for the next set of PD measurements.
Since the calculated index of PD measurements always includes the results from all measurement dates and since the confidence level of this tool will increase with more measurement campaigns, it is probably wise not to base the PD diagnostic on a single set of measurements.
However, in cases where critical features appear from interpretation of PDAH
results, it will be better to react faster with additional diagnostics: visual observation for PD
identified as external or BARTACT for slot PD, for example.
When new results are measured by field personnel with any of the diagnostic tools, the data are transferred to the system using the appropriate window. For PD
measurement for instance, the user has only to click on the transfer button at the bottom of the screen in Figure 8 (circled). The system, without any other human intervention, accepts the predetermined file format, integrates the new files in the database, calculates a new value for the individual index with its confidence level for this tool and aggregates it with all other existing tools to get a global diagnostic index for the generator. Therefore, 1 or 2 min after a transfer, all users have access to the new data as well as new index and confidence.
Visual inspection in the calculation of indexes with confidence is included in the system in the same way as any other tool, with the calculations transparent for users.
The differences for visual inspection are that there is no numerical file to transfer and the information from a visual inspection can depend on the person performing this task. Certain training may be necessary to make sure all observe the same component in the same detail, using the same approach. The precision of the readings can also be slightly reduced for greater simplicity. The readings are converted into a numerical index of 1 to 5 for any observation made, regardless of the components observed.
One approach used can be that of associating pictures with each component in a known state of degradation. For example, Figure 9 shows three pictures illustrating contamination in the end arms of stator windings. Three degrees of severity are shown (high, average, low). The user clicks on the appropriate boxes to register the level of contamination, a high level of contamination, or average or low contamination.
If the high-contamination box is checked, it means that similar or worse contamination was observed on the machine's end arms. In addition to the severity, the user indicates the spread of contamination over the generator (generalized to the entire machine, observed only on some of the end arms, or located only in limited areas).
Thus, one of the three circles beside the text "Generalisee/Rependue/Localisee"
(widespread/spread/localized) is marked for each severity level present. The connection end and opposite connection end of the generator can be marked independently, depending on whether they are accessible. Air baffles sometimes prevent observation of one or both ends and this will have an impact on the confidence level for this component.
Visual inspection is a two-step process, the first one being the reading in the field and the second, logging the information in the system. Using the same interface as field data sheet as the one displayed in the system eliminates the risk of discrepancies.
Once the observations for this component are logged, the user confirms the information by clicking the button at the bottom of the screen to automatically generate a value of 1 to 5 (better to worse) for this degradation sign according to predefined rules.
There is a logging interface for each sign of degradation for every component and sub-component of the generator. Each interface uses characteristic pictures showing different severities and distributions in the generator. For instance, the number of end-arms affected by traces of corona PD degradation at the junction of the grading system can be counted and logged in one of three groups of severity, whereas other components or signs of degradation are quantified with more macroscopic rules.
For most generators, a visual inspection will only reveal a few signs or no signs of degradation. Therefore, only a few signs require opening a detailed quantification window, similar to that in Figure 9, to make the visual association with the proposed pictures. In addition to all detailed quantification interfaces, the system application uses a main visual inspection interface to quickly log and display the results of an inspection. This main window is displayed on the right in Figure 10. To facilitate the transfer process, the user can click in this single window all the boxes for signs where no anomalies were observed and this will automatically result in a value of 1Ø Any detailed quantification window can be accessed by clicking directly on the name of the sign in Figure 10 and, once the information is logged and accepted, the main window comes back on screen with a calculated index for this particular sign.
One major advantage of the system according to the invention is that it offers a platform for managers and maintenance engineers to communicate using common facts (global indexes and individual tool indexes) about the condition of the units. An example of this is shown in Figure 6, where plant C is selected by clicking in the tree of the global classification interface (Figure 4) to reveal the global indexes of the four units. The right-hand side shows graphically the same information as that appearing in the tree (in Figure 6, "cote" represents the global index of the unit). In plant C, it can be seen that unit 1 is the worst with an index of 4.8 and a confidence level of 21%, whereas unit 2 is the best at 2.2 and 63%. The difference in confidence levels stems from the combination of tools used in the diagnostic, the number of measurement campaigns and the date of the last measurement (more recent measurements give a higher confidence than older ones). As time passes, the application automatically updates the confidence levels without human intervention. It is easy to see that managers do not need to be specialists in generator diagnostics to understand this information, which is accessible at any time directly from their personal computer. Up to this point, anyone, with no knowledge about interpretation of PD, ozone or DC
ramp test measurements, for instance, can access the diagnostic. If a plant manager is more concerned about a specific generator, such as unit 1 in the example of Figure 6, he can than call for help from a specialist if desired.
The next level of information is obtained by clicking directly on the unit number in the tree, which reveals the calculated individual index for each tool used in the diagnostic, as illustrated in Figure 7. Here the detailed results of unit 1 came only from PD
measurements made with the PDAH, whereas the diagnostic of unit 2 was the result of the aggregation of five different tools (BARTACT, visual inspection, ITERG, PDAH
and PRPD). This is why the confidence level of unit 1 (21 %) is lower than that of unit 2 (63%).
The system has a measurement procedure for each tool and, when it is respected, the confidence level will be higher than when it is not or only partially respected. For instance, sometimes it is not possible to carry out an entire test for lack of time or accessibility. When the data from a tool is transferred, automatic routines calculate both the index and the confidence level according to predefined rules, eliminating the subjectivity in the process.
In the example of Figure 7, it can be seen for unit 2 that the confidence level of the different diagnostic tools ranges from 97% for the PDAH down to 47% for the visual inspection. The high confidence level for PD suggests that the measurements were done in accordance with prescriptions, and that the last measurement is recent. The lower confidence for the visual inspection may indicate that too few components were inspected or that the last inspection dates back too far. Up to this level, all the information is easy to understand and allows recognizing that unit 1 is potentially in worse condition than unit 2. To confirm this, the specific PD results for unit 1 may be analyzed to understand the reason for this high index if desired. According to the detailed analysis of PD measurements, other complementary tests can be prescribed.
The detailed results for any diagnostic tool can be accessed by anyone interested. To access the results, the user only has to click on the name of the tool he/she wants to study below the number of the unit in the tree, such as the one in Figure 7.
This displays the corresponding results in the right-hand portion of the screen, as illustrated in Figure 8 for the PDAH of unit 1. The twelve graphs presented here are those of twelve couplers installed on this generator (one per parallel circuit of each of the three phase windings). Positive (darker) and negative (lighter) discharges can be compared in each histogram. Other quantities, such as amplitude, discharge rate, NQN, maximum amplitude and positive/negative ratio can all be extracted from the display. On the date of these measurements there were four data series, corresponding to the different gain settings used that day. Each series can be displayed just by clicking on the appropriate series number in the tree. Here, only one date appears but, when several measurement campaigns exist, they can all be displayed and analyzed. The detailed analysis allows determining if slot discharge, end-winding discharges or internal delamination might be present. Based on the conclusion reached, other tests can be arranged to obtain a more thorough diagnostic with an appreciation of the problem with higher level of confidence.
Sometimes, when the degradation mode identified is considered to be slow, the conclusion may be to wait one more year for the next set of PD measurements.
Since the calculated index of PD measurements always includes the results from all measurement dates and since the confidence level of this tool will increase with more measurement campaigns, it is probably wise not to base the PD diagnostic on a single set of measurements.
However, in cases where critical features appear from interpretation of PDAH
results, it will be better to react faster with additional diagnostics: visual observation for PD
identified as external or BARTACT for slot PD, for example.
When new results are measured by field personnel with any of the diagnostic tools, the data are transferred to the system using the appropriate window. For PD
measurement for instance, the user has only to click on the transfer button at the bottom of the screen in Figure 8 (circled). The system, without any other human intervention, accepts the predetermined file format, integrates the new files in the database, calculates a new value for the individual index with its confidence level for this tool and aggregates it with all other existing tools to get a global diagnostic index for the generator. Therefore, 1 or 2 min after a transfer, all users have access to the new data as well as new index and confidence.
Visual inspection in the calculation of indexes with confidence is included in the system in the same way as any other tool, with the calculations transparent for users.
The differences for visual inspection are that there is no numerical file to transfer and the information from a visual inspection can depend on the person performing this task. Certain training may be necessary to make sure all observe the same component in the same detail, using the same approach. The precision of the readings can also be slightly reduced for greater simplicity. The readings are converted into a numerical index of 1 to 5 for any observation made, regardless of the components observed.
One approach used can be that of associating pictures with each component in a known state of degradation. For example, Figure 9 shows three pictures illustrating contamination in the end arms of stator windings. Three degrees of severity are shown (high, average, low). The user clicks on the appropriate boxes to register the level of contamination, a high level of contamination, or average or low contamination.
If the high-contamination box is checked, it means that similar or worse contamination was observed on the machine's end arms. In addition to the severity, the user indicates the spread of contamination over the generator (generalized to the entire machine, observed only on some of the end arms, or located only in limited areas).
Thus, one of the three circles beside the text "Generalisee/Rependue/Localisee"
(widespread/spread/localized) is marked for each severity level present. The connection end and opposite connection end of the generator can be marked independently, depending on whether they are accessible. Air baffles sometimes prevent observation of one or both ends and this will have an impact on the confidence level for this component.
Visual inspection is a two-step process, the first one being the reading in the field and the second, logging the information in the system. Using the same interface as field data sheet as the one displayed in the system eliminates the risk of discrepancies.
Once the observations for this component are logged, the user confirms the information by clicking the button at the bottom of the screen to automatically generate a value of 1 to 5 (better to worse) for this degradation sign according to predefined rules.
There is a logging interface for each sign of degradation for every component and sub-component of the generator. Each interface uses characteristic pictures showing different severities and distributions in the generator. For instance, the number of end-arms affected by traces of corona PD degradation at the junction of the grading system can be counted and logged in one of three groups of severity, whereas other components or signs of degradation are quantified with more macroscopic rules.
For most generators, a visual inspection will only reveal a few signs or no signs of degradation. Therefore, only a few signs require opening a detailed quantification window, similar to that in Figure 9, to make the visual association with the proposed pictures. In addition to all detailed quantification interfaces, the system application uses a main visual inspection interface to quickly log and display the results of an inspection. This main window is displayed on the right in Figure 10. To facilitate the transfer process, the user can click in this single window all the boxes for signs where no anomalies were observed and this will automatically result in a value of 1Ø Any detailed quantification window can be accessed by clicking directly on the name of the sign in Figure 10 and, once the information is logged and accepted, the main window comes back on screen with a calculated index for this particular sign.
The aggregation of all signs is calculated with respect to the weight of every component and sub-component. The more signs are logged in, the higher the confidence level, suggesting that this visual inspection represents the overall condition of the generator. The solution of using simple picture associations to convert a priori subjective information into a numerical index for visual inspection through quantification algorithms mimicking experts reasoning results in an index that can then be treated mathematically the same way as the other tools in the aggregation of the global diagnostic of the generator.
In addition to the display of simplified and detailed results used to make a diagnostic at a specific point in time, it is also of interest to trend the evolution of the global index for a generator over time, and of individual indexes for each tool. The system can offer user-friendly trending options, like the three as schematized in the third column on Figure 3. They go from the macroscopic display of how the entire fleet evolves over time (getting better or worse), to a microscopic view of the change in condition of any generator. An intermediate level allows a display of all the generators at a plant.
Figure 11 illustrates the evolution of the diagnostic of a single unit over time, in this case for generator 2 at plant C. This display is obtained simply by clicking on the label "Historique" in the tree of the global classification underneath the number of the unit, as shown in Figure 7.
In the example of Figure 11, it can be seen that the global diagnostic is obtained by the aggregation of five diagnostic tools (PDAH, ITERG, BARTACT, PRPD and Visual Inspection) and a sixth element, which show maintenance interventions on the unit over the years. Each adjacent bar in the histogram represents a different tool, showing the evolution of the results over time. The global diagnostic, represented by the black line, is calculated from different indexes.
At any point in time, only the tools used up to that date compose the global condition index of the machine. For instance, before September 2002, the global diagnostic came from aggregation of the PDAH (first bar), BARTACT (third bar) and ITERG
(second bar). Since the PDAH readings were high with an individual index of 4.8, in December of that same year, a PRPD measurement (fourth bar) was performed to identify the exact nature of the discharge sources. Detailed analysis of those results revealed that the intense PD activity mainly came from gap type discharges occurring in the end-arm region of the winding. Later in 2008, when the generator was accessible, a visual inspection was made and the reading was logged in (fifth bar).
The individual index of the visual inspection was low at 2.2 and the only signs showing values different from unity (see Figure 10) were: a slight red powder accumulation, waving of the stator yoke and the presence of conductive debris on the circuit rings which were removed. The low index of the visual inspection contributed to further improving the generator's global condition index.
One other piece of information appearing on this graph is the presence of the maintenance interventions during the generator's life. In Figure 11, only one intervention is present, marked by the bar extending from the bottom to the top of the graph. When the user moves the mouse over this bar, it shows the nature of the intervention. Some interventions, which can be added for example from a list of 45 predefined actions, can have an immediate impact on the diagnostic, such as a rewind or. a restacking of the unit. When such actions are logged in, all the results from previous measurements are automatically reinitialized. However, for other actions, such as solvent cleaning of the overhang like that appearing in Figure 11 (2002/11/01), the effectiveness of the procedure cannot be assumed anymore than its effect on the measurements. Thus, for this action, all results are left as they were but, to anyone looking at the graph in Figure 11, it is clear that here the cleaning was successful, resulting in a reduction of PD activity and, consequently, a sustained reduction of the individual and global indexes.
This level of detail is of great value to the maintenance specialist but managers, more concerned about the overall reliability of their entire installations, will be interested in a more macroscopic analysis of the plant. The second level of "Historique" for the three generators of plant M is illustrated in Figure 12. Here a slow overall degradation of the units over time is observed, with unit 13 being the worst and 11 the best. At this level, it is not possible to know why unit 13 is in such a poor condition but, if the plant manager is concerned about it, he/she can ask a specialist to have a look at the details and give him/her the reason behind this global diagnostic.
The last portion of the application, appearing in the last column in Figure 3, is the documentation section. The first item of the section is the generation of condition reports, based on automatic algorithms, which may be designed not to replace maintenance specialists but rather help them out in proposing guidelines for their diagnostics. These reports also have the advantage of helping new personnel to learn the difficult process of generator diagnosis.
Two levels of report may exist: for a plant or for a specific unit. As for trending at the unit level, data are analyzed for each tool and a summary of the analysis is found in the report with the global condition index of the machine. Specific comments associated with the automated analysis of the results of each tool are listed, such as the potential source of PD for the PRPD or whether the intrinsic insulation quality is acceptable for the ITERG measurements. The more general report at the plant level will give a classification of the generators in different categories as shown in the following example:
= List of generators with a very high global condition index:
None = List of generators with a high global condition index:
None = List of generators with an intermediate global condition index:
04, Index = 2, 6, Confidence = 69%
06, Index = 3,0, Confidence = 68%
08, Index = 2,7, Confidence = 43%
= List of generators with a low global condition index:
01, Index = 1,4, Confidence = 77%
02, Index = 1,2, Confidence = 43%
03, Index = 1,9, Confidence = 72%
05, Index = 1,2, Confidence = 43%
07, Index = 1,4, Confidence = 56%
09, Index = 1,2, Confidence = 43%
In addition to the display of simplified and detailed results used to make a diagnostic at a specific point in time, it is also of interest to trend the evolution of the global index for a generator over time, and of individual indexes for each tool. The system can offer user-friendly trending options, like the three as schematized in the third column on Figure 3. They go from the macroscopic display of how the entire fleet evolves over time (getting better or worse), to a microscopic view of the change in condition of any generator. An intermediate level allows a display of all the generators at a plant.
Figure 11 illustrates the evolution of the diagnostic of a single unit over time, in this case for generator 2 at plant C. This display is obtained simply by clicking on the label "Historique" in the tree of the global classification underneath the number of the unit, as shown in Figure 7.
In the example of Figure 11, it can be seen that the global diagnostic is obtained by the aggregation of five diagnostic tools (PDAH, ITERG, BARTACT, PRPD and Visual Inspection) and a sixth element, which show maintenance interventions on the unit over the years. Each adjacent bar in the histogram represents a different tool, showing the evolution of the results over time. The global diagnostic, represented by the black line, is calculated from different indexes.
At any point in time, only the tools used up to that date compose the global condition index of the machine. For instance, before September 2002, the global diagnostic came from aggregation of the PDAH (first bar), BARTACT (third bar) and ITERG
(second bar). Since the PDAH readings were high with an individual index of 4.8, in December of that same year, a PRPD measurement (fourth bar) was performed to identify the exact nature of the discharge sources. Detailed analysis of those results revealed that the intense PD activity mainly came from gap type discharges occurring in the end-arm region of the winding. Later in 2008, when the generator was accessible, a visual inspection was made and the reading was logged in (fifth bar).
The individual index of the visual inspection was low at 2.2 and the only signs showing values different from unity (see Figure 10) were: a slight red powder accumulation, waving of the stator yoke and the presence of conductive debris on the circuit rings which were removed. The low index of the visual inspection contributed to further improving the generator's global condition index.
One other piece of information appearing on this graph is the presence of the maintenance interventions during the generator's life. In Figure 11, only one intervention is present, marked by the bar extending from the bottom to the top of the graph. When the user moves the mouse over this bar, it shows the nature of the intervention. Some interventions, which can be added for example from a list of 45 predefined actions, can have an immediate impact on the diagnostic, such as a rewind or. a restacking of the unit. When such actions are logged in, all the results from previous measurements are automatically reinitialized. However, for other actions, such as solvent cleaning of the overhang like that appearing in Figure 11 (2002/11/01), the effectiveness of the procedure cannot be assumed anymore than its effect on the measurements. Thus, for this action, all results are left as they were but, to anyone looking at the graph in Figure 11, it is clear that here the cleaning was successful, resulting in a reduction of PD activity and, consequently, a sustained reduction of the individual and global indexes.
This level of detail is of great value to the maintenance specialist but managers, more concerned about the overall reliability of their entire installations, will be interested in a more macroscopic analysis of the plant. The second level of "Historique" for the three generators of plant M is illustrated in Figure 12. Here a slow overall degradation of the units over time is observed, with unit 13 being the worst and 11 the best. At this level, it is not possible to know why unit 13 is in such a poor condition but, if the plant manager is concerned about it, he/she can ask a specialist to have a look at the details and give him/her the reason behind this global diagnostic.
The last portion of the application, appearing in the last column in Figure 3, is the documentation section. The first item of the section is the generation of condition reports, based on automatic algorithms, which may be designed not to replace maintenance specialists but rather help them out in proposing guidelines for their diagnostics. These reports also have the advantage of helping new personnel to learn the difficult process of generator diagnosis.
Two levels of report may exist: for a plant or for a specific unit. As for trending at the unit level, data are analyzed for each tool and a summary of the analysis is found in the report with the global condition index of the machine. Specific comments associated with the automated analysis of the results of each tool are listed, such as the potential source of PD for the PRPD or whether the intrinsic insulation quality is acceptable for the ITERG measurements. The more general report at the plant level will give a classification of the generators in different categories as shown in the following example:
= List of generators with a very high global condition index:
None = List of generators with a high global condition index:
None = List of generators with an intermediate global condition index:
04, Index = 2, 6, Confidence = 69%
06, Index = 3,0, Confidence = 68%
08, Index = 2,7, Confidence = 43%
= List of generators with a low global condition index:
01, Index = 1,4, Confidence = 77%
02, Index = 1,2, Confidence = 43%
03, Index = 1,9, Confidence = 72%
05, Index = 1,2, Confidence = 43%
07, Index = 1,4, Confidence = 56%
09, Index = 1,2, Confidence = 43%
10, Index = 1,8, Confidence = 69%
11, Index = 1,1, Confidence = 43%
12, Index = 1, 2, Confidence = 43%
= List of generators with no global condition index:
None To access each of these diagnostic reports, the user clicks on the label "Diagnostic"
at the plant or unit level as indicated in Figure 13.
Other documents found in this section and permanently accessible from anywhere in the utility, are measurement procedures for every diagnostic tool used on site, with all the details on how to prepare and perform the measurements. In addition, simple one-page job aids are also supplied so that field personnel can effectively carry out any measurement without having to read an extensive report. The user's manual for the diagnostic site is also found in this section. This manual explains how to transfer new data and display results and trending. Among other things, it contains reports on the theory of each type of measurement. The documentation section is not static and accepts new reports, case studies or any other documents that are of interest to the personnel involved in performing diagnostics.
The system according to the invention allows migrating from preventive maintenance to CBM so as to become more efficient in generator diagnostics and leave a permanent trace of every diagnostic performed. In the past, there were no standardized tools to characterize a generator and, depending on the team doing the diagnostic, the measurements and inspection could differ. Moreover, most of the results were difficult to find and, when they were located, specialists needed to analyze the data and produce a report that would often leave few traces over time.
Thus, after changes in personnel or retirement of those involved, it would be a tedious task to find the information a few years later. The system circumvents the problem of data mining. The centralized data bank is a center point of the approach to ensure all diagnostic measurements from every tool are always be readily accessible.
Computation of simple individual indexes per tool and their aggregation to come up with an integrated diagnostic comprised the second step of the process. Since this approach is evidence-based, it would be difficult to make an integrated diagnostic if there is no way to access all relevant information quickly and on request.
Moreover, maintenance can only be optimized if all diagnostics are constantly updated and accessible to specialists and managers.
Adjustments can be made to the system and if every algorithm, quantification rule and aggregation principles is thoroughly documented in reports and continuously updated, it is possible to build the entire approach systematically, one step at a time. This common knowledge makes sure that the ensuing diagnostic is as objective as possible and reflects the facts as measured on the generators.
Another advantage of having fixed diagnostic rules is that now generator diagnostics are less variable and do not depend on the person performing a test or a set of tests.
In addition, the selection of the tests to be performed is now organized and no longer subjective: Level-2 tests are generally triggered by Level-1 results and so on. The set of complementary diagnostic tools used is selected in terms of achieving a more comprehensive diagnostic. New tools can be added on an as-needed basis. The modular approach ensures that addition of any tool is simple. Calculation of the global diagnostic index for a generator is defined in a way that the diagnostic is always valid regardless of the number or combination of tools used. However, depending on the selection and number of tools, the confidence level will change to reflect how well the diagnostic truly represents the overall condition of a generator. The advantage of having a single system using a Web-based approach is that, from one day to the next, if one algorithm is modified, it is automatically accessible to everyone and the change is entirely transparent to the user.
In itself, compiling diagnostic data on a single server is already a major improvement compared to past practices. With the system according to the invention, anyone, from anywhere in the utility, can check this information in a few minutes. In addition, it is possible for any user to know if the index is good or bad, see its trend over time and get a brief report, and this is true for any diagnostic tool. The strength of the approach is also to have this user-friendly system with simple accessible information, without losing any information about the detailed files when the diagnostic needs to be carried further.
= List of generators with no global condition index:
None To access each of these diagnostic reports, the user clicks on the label "Diagnostic"
at the plant or unit level as indicated in Figure 13.
Other documents found in this section and permanently accessible from anywhere in the utility, are measurement procedures for every diagnostic tool used on site, with all the details on how to prepare and perform the measurements. In addition, simple one-page job aids are also supplied so that field personnel can effectively carry out any measurement without having to read an extensive report. The user's manual for the diagnostic site is also found in this section. This manual explains how to transfer new data and display results and trending. Among other things, it contains reports on the theory of each type of measurement. The documentation section is not static and accepts new reports, case studies or any other documents that are of interest to the personnel involved in performing diagnostics.
The system according to the invention allows migrating from preventive maintenance to CBM so as to become more efficient in generator diagnostics and leave a permanent trace of every diagnostic performed. In the past, there were no standardized tools to characterize a generator and, depending on the team doing the diagnostic, the measurements and inspection could differ. Moreover, most of the results were difficult to find and, when they were located, specialists needed to analyze the data and produce a report that would often leave few traces over time.
Thus, after changes in personnel or retirement of those involved, it would be a tedious task to find the information a few years later. The system circumvents the problem of data mining. The centralized data bank is a center point of the approach to ensure all diagnostic measurements from every tool are always be readily accessible.
Computation of simple individual indexes per tool and their aggregation to come up with an integrated diagnostic comprised the second step of the process. Since this approach is evidence-based, it would be difficult to make an integrated diagnostic if there is no way to access all relevant information quickly and on request.
Moreover, maintenance can only be optimized if all diagnostics are constantly updated and accessible to specialists and managers.
Adjustments can be made to the system and if every algorithm, quantification rule and aggregation principles is thoroughly documented in reports and continuously updated, it is possible to build the entire approach systematically, one step at a time. This common knowledge makes sure that the ensuing diagnostic is as objective as possible and reflects the facts as measured on the generators.
Another advantage of having fixed diagnostic rules is that now generator diagnostics are less variable and do not depend on the person performing a test or a set of tests.
In addition, the selection of the tests to be performed is now organized and no longer subjective: Level-2 tests are generally triggered by Level-1 results and so on. The set of complementary diagnostic tools used is selected in terms of achieving a more comprehensive diagnostic. New tools can be added on an as-needed basis. The modular approach ensures that addition of any tool is simple. Calculation of the global diagnostic index for a generator is defined in a way that the diagnostic is always valid regardless of the number or combination of tools used. However, depending on the selection and number of tools, the confidence level will change to reflect how well the diagnostic truly represents the overall condition of a generator. The advantage of having a single system using a Web-based approach is that, from one day to the next, if one algorithm is modified, it is automatically accessible to everyone and the change is entirely transparent to the user.
In itself, compiling diagnostic data on a single server is already a major improvement compared to past practices. With the system according to the invention, anyone, from anywhere in the utility, can check this information in a few minutes. In addition, it is possible for any user to know if the index is good or bad, see its trend over time and get a brief report, and this is true for any diagnostic tool. The strength of the approach is also to have this user-friendly system with simple accessible information, without losing any information about the detailed files when the diagnostic needs to be carried further.
When a specialist gets a call from a plant manager, he/she can now, in a few minutes, consult the information about a specific generator while looking at the same results as the manager on the screen. By being connected to the system, they now share the same data simultaneously and can work together on a course of action according to the planned outage schedule of the unit.
The above described integrated diagnostic system for generators can be used utility-wide and is accessible by Intranet from anywhere. The system accepts measurement results from diagnostic tools/techniques including visual inspection. As soon as new results are transferred to the corporate server hosting the application, automatic calculations are performed generating simple conditions indexes and an integrated diagnostic for the generator. The classification of all generators, from worst to best, is continuously updated and displayed to identify where maintenance intervention is most likely required. This common source of information is based on validated quantification rules making generator diagnostics quick, systematic and less subjective. The system facilitates the transition from time-based maintenance to condition-based maintenance.
Although the system has been described above in reference with generators, it should be noted that it can be also used for condition-based maintenance of other complex equipment and structures.
The above described integrated diagnostic system for generators can be used utility-wide and is accessible by Intranet from anywhere. The system accepts measurement results from diagnostic tools/techniques including visual inspection. As soon as new results are transferred to the corporate server hosting the application, automatic calculations are performed generating simple conditions indexes and an integrated diagnostic for the generator. The classification of all generators, from worst to best, is continuously updated and displayed to identify where maintenance intervention is most likely required. This common source of information is based on validated quantification rules making generator diagnostics quick, systematic and less subjective. The system facilitates the transition from time-based maintenance to condition-based maintenance.
Although the system has been described above in reference with generators, it should be noted that it can be also used for condition-based maintenance of other complex equipment and structures.
Claims (2)
1. A diagnostic system for condition-based maintenance of a complex equipment or structure, comprising:
a transfer interface for receiving and processing tool associated measurement data indicative of predetermined conditions of the complex equipment or structure;
a centralized database connectable to the transfer interface for receiving and storing raw results derived from the processed data along with tool origin information associated with the raw results;
an index calculation unit connectable to the centralized database for determining individual condition indicative indexes from the raw results based on the corresponding tool origin information and determining respective confidence degrees that the individual condition indicative indexes are representative of an actual condition of the complex equipment or structure based on predefined rules;
an aggregation unit connectable to the index calculation unit for combining the raw results collected through time as functions of the individual condition indicative indexes, the respective confidence degrees and predefined tool respective weighting parameters and determining a global index from the combined results and a corresponding confidence degree that the global index is representative of an actual general condition of the complex equipment or structure based on predefined rules;
and a diagnosis interface connectable to the aggregation unit for processing the raw results, the indexes and confidence degrees and providing maintenance oriented diagnostic information according to predefined diagnostic levels as functions of the tool origin information.
a transfer interface for receiving and processing tool associated measurement data indicative of predetermined conditions of the complex equipment or structure;
a centralized database connectable to the transfer interface for receiving and storing raw results derived from the processed data along with tool origin information associated with the raw results;
an index calculation unit connectable to the centralized database for determining individual condition indicative indexes from the raw results based on the corresponding tool origin information and determining respective confidence degrees that the individual condition indicative indexes are representative of an actual condition of the complex equipment or structure based on predefined rules;
an aggregation unit connectable to the index calculation unit for combining the raw results collected through time as functions of the individual condition indicative indexes, the respective confidence degrees and predefined tool respective weighting parameters and determining a global index from the combined results and a corresponding confidence degree that the global index is representative of an actual general condition of the complex equipment or structure based on predefined rules;
and a diagnosis interface connectable to the aggregation unit for processing the raw results, the indexes and confidence degrees and providing maintenance oriented diagnostic information according to predefined diagnostic levels as functions of the tool origin information.
2. A diagnostic system or method comprising any feature described, either individually or in combination with any feature, in any configuration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA2666894A CA2666894A1 (en) | 2009-05-27 | 2009-05-27 | System for condition-based maintenance of complex equipment and structures |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CA2666894A CA2666894A1 (en) | 2009-05-27 | 2009-05-27 | System for condition-based maintenance of complex equipment and structures |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA2666894A1 true CA2666894A1 (en) | 2010-11-27 |
Family
ID=43242680
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA2666894A Abandoned CA2666894A1 (en) | 2009-05-27 | 2009-05-27 | System for condition-based maintenance of complex equipment and structures |
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| Country | Link |
|---|---|
| CA (1) | CA2666894A1 (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105324900A (en) * | 2013-04-22 | 2016-02-10 | Abb技术有限公司 | Method and apparatus for defect pre-warning of power device |
-
2009
- 2009-05-27 CA CA2666894A patent/CA2666894A1/en not_active Abandoned
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105324900A (en) * | 2013-04-22 | 2016-02-10 | Abb技术有限公司 | Method and apparatus for defect pre-warning of power device |
| EP2989705A4 (en) * | 2013-04-22 | 2017-01-25 | ABB Technology Ltd. | Method and apparatus for defect pre-warning of power device |
| CN105324900B (en) * | 2013-04-22 | 2018-02-06 | Abb技术有限公司 | The method and apparatus of the early warning of the defects of for power equipment |
| US10613153B2 (en) | 2013-04-22 | 2020-04-07 | Abb Schweiz Ag | Method and apparatus for defect pre-warning of power device |
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