Intelligent Tutoring System for Partial Discharges with Virtual Reality Diego Hurtado1, Alberto Ochoa1,2
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Instituto Nacional de Electricidad y Energías Limpias, Gerencia de Tecnologías de la Información, Reforma 113, 62490 Cuernavaca, México 2 Universidad Autónoma de Ciudad Juárez, Maestría en Computo Aplicado, Av. Plutarco Elías Calles 1210, 32310 Cd. Juárez, México
[email protected], alberto.ochoa@ uacj.mx 1
Abstract. The electrical domain requires efficient and well trained personnel, due to the danger involved in the partial discharges field, qualified electricians are required. In this paper present a prototype proposal for developing an intelligent tutoring system. We propose a model domain of a subset of partial discharges and a trainee model which represents the affective and knowledge states of trainees. We also propose a user interface with virtual reality, since it is hypothesized that the trainees will able to learn the electrical domain installations of partial discharges and gain knowledge in a more efficient way than trainees using traditional training methods. Keywords: Intelligent tutoring system, artificial intelligence, virtual reality, partial discharges.
1 Introduction An intelligent tutoring system provides students with the same instructional advantage that a sophisticated human tutor can provide [1]. From its beginnings, the computer has been viewed as capable of providing such instruction, thereby having the potential to improve the quality of education. Advances in artificial intelligence techniques have provided more efficient methods for achieving intelligent programs. Furthermore, artificial intelligence and cognitive psychology have also meant real gains in the time to create lessons. The goal is to create a lesson, at rate that is faster than more the 200 to 1 typically cited for conventional educational software. It is not necessary to specify every interaction with the student, but only the general problemsolving principles from which these interactions can be generated. A potential application of intelligent tutoring systems (ITS) is the training of operators of electrical installations. In this paper, we present current results in developing an intelligent tutoring system for learning partial discharges for the laboratory of equipment and materials testing. One of the most urgent needs is to develop an Intelligent Tutor for learning partial discharges, which will serve as a tool for engineers to learn and interpret their process instructions in order to make decisions. There are many computer tools in teaching, which become computer-assisted learning through the use of Artificial Intelligence techniques. It is important to minimize the random error in the training of engineers
for the learning and manipulation of the test laboratories, since this is especially important for the management of the laboratories, so that make learning more effective, efficient and reliable to see if something useful comes out of them, however, it is always important to find the right way to teach and validate what is interpreted. This process becomes more complex when people have different ways of grasp information; this is why it is important to specify the type of meaningful learning that is required. One of the most viable alternatives for specifying the type of meaningful learning that is required for each user is through the recognition of patterns and trait of the information, since it identifies, characterizes, classifies and reconstructs behaviors of processes important for the decision making. The laboratory of equipment and materials testing aims to meet the needs of the national and international electricity sector, providing specialized engineering studies, laboratory and field tests to equipment and materials, as well as quality management of supplies and systems, in a reliable, timely and effective manner, based on a high degree of specialization of its personnel, with a permanent service attitude. We are working to develop a non-immersive virtual reality training system for electrical partial discharge measurement. We want to build a student model based on the data log of this system.
2 Intelligent training system The modeling of intelligent tutoring systems is a complex task, since it is necessary to consider the three basic modules of the architecture proposed by Carbonell [2].
Fig. 1. Modeling of intelligent tutoring systems to partial discharges.
In order to design the intelligent tutor, we must include each of the items in the domain model, the measurable model of the capacitor and the set of strategies that allows to specify the type of significant learning that is required and to store the information in a test repository associated with the student model. This
communication proposes a remodeling of the tutor model to meet the needs of the operators of partial discharges testing laboratories. Partial discharges, as defined by IEC 60270, are localized dielectric discharges in a partial area of an electrical insulation system under high electric field intensity. The detection and monitoring of partial discharges (PD) is of vital importance because PD phenomena often precede an insulation breakdown of high voltage equipment leading to cost intensive outages and repairs. For this reason, for many years generators, transformers, switchgear and cable systems have been checked for partial discharge. The partial discharges domain requires efficient and well trained operators due to the risk involved because of a badly performed can result on accidents that can injure the workers and even cause the dead, or damage costly equipment and the limited availability of the laboratory of partial discharges that trainees need for practicing in real environments. The limited opportunity to practice in real environments makes training operators to take too long besides being costly and the danger involved. In order to solve this problem, we are working in an Intelligent Training System for partial discharges using virtual reality systems. The training scenarios are presented as virtual reality environments enabling the operators practice and learn before going to the testing laboratories. In our proposal, operators will use the system by learning and practice aided by an intelligent training system. The intelligent training system integrated virtual reality systems which allows having a virtual representation of the testing laboratory making this even more realistic. The int llig nt tutoring syst m comp r s th tr in work with xp rt’s solutions mo ls the operators probably knowledge of domain and provides coaching or advice with the use of virtual reality. Furthermore, it is hoped that this research will have a positive impact on supporting operators learning the partial discharges. They involved operators in practicing in real environments to solve a given problem. These technologies are helpful tools to support and improve the knowledge of the operators. The intelligent training system is the component that allows the adaptation for particular needs for each operator. It includes elements of intelligent tutoring systems such as the adaptation of particular needs to the student model [3].
Fig. 2. Intelligent training model
The architecture of DEPTHS (Fig. 3) follows the main guidelines for ITS architecture. In particular, it is widely agreed that the major functional components of an ITS architecture should be: the domain knowledge model, the student model, the pedagogical module, and the user interface [4] Accordingly, the DEPTHS architecture consists of Pedagogical Module, Expert Module, Student Model, Domain Model, and Presentation module [5]. The user interface component of the traditional architecture is adapted to the virtual reality environment and is the Presentation Module in our model.
Fig. 3. DEPTHS system architecture
The intelligent training system have shown the benefits of simulations environments for training [6] they have lower operating costs and are safer to use than in Extra High Voltage, High Voltage and Medium Voltage, High Power in Medium and Low Volt g nvironm nt th t rm ‘virtu l nvironm nt’ r f rs to th xp ri nc presented to the user by the system. Electric simulators also allow the simulation of dangerous scenarios not allowable with real High Voltage Laboratory. Furthermore, the difficulty with the current generation of electric simulations is that they are not readily used for other training tasks. The simulator allows the user to interact with the design of the high voltage laboratory to some extent as well using the virtual reality environment to start the training. We now turn to the development of a model which satisfy the requirements of training computer interfaces.
Fig. 4. Adaptive Learning for partial discharges with Virtual Reality Systems.
The trainee model represents the knowledge and affective states of trainees and their profile. The information in this model is useful for instructors to adapt the instruction in classroom, to plan the lesson and finally grant a certification that endorse that the student successfully complete the training. This model can be useful to design new lessons and new testing materials and even redesign training material to adapt it for new and even better lessons. The trainee interacts with the intelligent training system via a virtual reality system.
3 Pedagogical Trainee Model Th p gogic l tr in mo l r pr s nts th tr in ’s knowledge about partial discharges topics include in the course. The model is updated when the trainee practices the electrical maneuvers and when he solves theoretical exams [7]. We are working on including three different trees for encoding uncertain expert knowledge depending of the style of learning, Visual learning, Auditory learning and Kinesthetic learning. we have not defined the complete structure and values of this Tree. Changing the structure of the node of the three, if the style if visual, the structure will change with more images, etc.
Fig. 7. Theoretical lesson, tree for a course with 4 topics of partial discharges, each topic is composed by a sequence of subtopics.
Fig. 8. Sub-topic, when the trainee chooses a lesson, depending of the Id of the node , makes a tree search of the lesson and it shows the information , the system load the data of the node in the same scene every time , just change the data.
After the presentation of one domain concept is completed, the system switches to the evaluation mode. In this mode, the system provides the student with exercises and tests in order to assess how much the student has learned about the concept [8]. If the student demonstrates insufficient knowledge of the concept, the system suggests the student with an appropriate learning path for that concept, different style of learning. As each question is connected with specific lesson(s), the system can easily provide links to the lessons which has to be relearned. The student can choose either to follow
the advice or to move on. After the repeated exploration of the suggested lesson(s), the student is presented with another test addressing the lessons that have been relearned. After each test, the system displays a page with student-directed feedback, including (but not limited to) the following: • th stu nt’s p rform nc on ch qu stion • the correct answer for each question, • the lessons that should be learned again, • th stu nt’s ov r ll knowl g of th t st om in concept • the correlation between this test result and his previous results, • many other statistical information, such as the time spent to solve the test, the difficulty level of each question, and points number on each question, corrects answers, incorrect answers, highest score.
Fig. 9. Theoretical test, which consist of questionnaires of multiple choice questions selected from a database, and they are marked automatically by the system.. The information obtained about the evaluation will let us know the level of knowledge of every node of the three, in this case the topic that is evaluated.
4 Virtual Reality System for Electrical Training We are working to develop several non-immersive virtual reality systems for training. The Virtual Reality System for Electrical Training, it includes lessons and practices for 6 measure environments, the goal is detection, classification and location of PD with measure of DP.
Fig. 10. Virtual Reality System for training on procedures to measure partials discharges, terminals for underground cable, electrical pole, electrical stations, Multi-channel measurements of power transformers, factories and laboratories. The trainee walk trough the scenario to choose a practical lesson, it
Fig. 11. Intelligent Training system with terminals for underground cable (Practical lesson with virtual reality) (Practical lesson with virtual reality)
Each Partial discharge measurement will be composed by a different number of steps, and in turn each step is composed by a different number of sub steps. At the beginning of each MPD the system also will include a training section where students should learn all the material, equipment and tools needed to perform the MPD. Thus, each MPD includes two sections, namely, selection of tools and development of a PDM throughout a series of steps and sub steps. The system provides students with facilities for these two sections to be learnt and practiced. In Table 1, a MPD step will consist of six sub-steps that will described. Each sub-step will consist of the description of the sub-step and the instruction; the instruction is an action to be executed by the trainee. The MPD described in Table 1 corresponds to the MPD shown in Fig. 11. Table 1. Example of a step of the Partial discharge measurement procedure. The step consists of four sub-steps.
Sub-step 1 Take the measuring equipment, in this case ULTRA TEV.
Instruction Take the ULTRA TEV Partial Discharge Analysis System from menu of tools 2 Choose an complement to ultra tev, that provides greater Take complement to ultra tev detail, depending of the environment. from menu of tools 3 Proceed to make the partial discharge measure with the Point the ultra tev to the asset equipment. Then a color screen shows details of PD activity as numerical and graphical values 4 Proceed to locate the exact site of an internal partial locate the exact site of PD discharge fault with ultra tear locator
Evaluations are organized in two separate sub evaluations: a) practical test, which consist in selecting tools and measurement of Partial discharge b) theoretical test, which consist of questionnaires of multiple choice questions selected from list of quizzes, and they are marked automatically by the system, described in Fig. 9 Table 2. Error types in the performance of measurement of partial discharge procedure. Error type 1 2 3 4
Description The trainee is trying to guess because he selected on the wrong element in the virtual environment. The trainee is trying to guess because he selected a tool which is not required for the MPD. This error is moderate. The trainee is unfamiliar with the interface; he selected on an element when it was asked to interact with the menu. This error is weak. The trainee was distracted because he selected a tool when a scene interaction was required.
5 Results In order to evaluate the student, the mo l r pr s nts th tr in ’s knowledge about partials discharges topics include in the course. The model is updated when the trainee practices the electrical maneuvers and when he solves theoretical exams. The model consists of a Tree Data Structure that simulates a Bayesian network. In turn each topic is composed by a sequence of subtopics. The three network is composed by a node for each partials discharges topic included in the course. In turn, each node of the tree representing a topic, composed by topics and subtopics. Initially, representing topics, the nodes stored all the information about that lesson and have two possible values: learnt and not learnt and their probabilities are conditionally dependent on the probabilities of learning the subtopics nodes. we asked an expert instructor to assert relationships between the factors of theoretical test. We are working on to store the a PDM, namely, steps, sub-steps, tools, errors. To evaluate the student, we compared the results of score the diagnostic test and the final test. The results of this evaluation are presented in Table 3; as it can be observed the precision of the data-driven model is around 60%. we need more experimentation and a more principled evaluation in order to have a comprehensive result. Table 3. Examples of Evaluation Test Database Evaluation Id 4 5 6 5 4
Trainee Id 93713 67856 YF022 YF022 YF022
DT 13% 10% 23% 23% 15%
Trained 50% 70% 75% 88% 80%
Attempts 2 1 2 3 5
Error % 50% 30% 25% 12% 20%
Fig. 11. Test Evaluation of the student, shows the highest score and compares the results of the diagnostic test and the final test.
Conclusions and future work
This research has analyzed the intelligent tutoring systems, we have presented the prototype architecture of a training virtual reality system for measurement of partial discharges operators, the creation of virtual work environment, and the course with 4 topics of measurements of partial discharges. We have sketched out a set of ideas that guide our approach towards the development of an affect sensitive intelligent tutoring system. With the first study complete, and the second in progress, a majority of the future efforts will be directed towards extensive Practical lesson with virtual reality. The training is focused on the knowledge of the measurement of partial discharges. The main goal is Improve the skills of the expert, so that there is no doubt in interpretation of the measure of partial discharges. we have sketched out an affective model using a tree data structure that simulates a Bayesian network, that build a course of a set of procedures/ techniques towards the development of an intelligent tutoring system using adaptive learning being incorporated to an intelligent tutor within a virtual laboratory for learning partial discharges measurement with Virtual Reality Systems. In this paper we describe a process model for developing affective intelligent tutoring systems with virtual reality system that includes an intelligent training model, Intelligent Training System Pedagogical Trainee Model and extensive evaluations. To improve training and to offer a better system to the customer, integrating both fields within training systems based on VR. This model includes an intelligent training providing adaptive learning and intelligent training since it recognizes the affect and knowledge state of trainees. Perhaps intelligent tutoring system can improve the state of knowledge of the trainee but still need the present of human instructors, instructors plays a decisive role in the training. As in other fields, training within the electrical field often involves high risk activities where mistakes are usually fatal. The system will train thousands of operators with the knowledge of measurement of partial discharges from the laboratories of proves of LAPEM. The general aim of our system is to provide operators of complex industrial environments with a suitable training to certify operators in knowledge, skills, expertise, abilities and attitudes for operation of measurement of partial discharges. As far as future work is concerned, we plan to consider the recognition of gestures jointly with face expressions corresponding to certain emotions in order to detect affective responses, which can express interest, excitement, confusion, etc. and suggest a review of the actual interaction flow
ACKNOWLEDGEMENTS This stu y h s n support y th nstituto nv stig cion s l ctric s xico Authors would like to thank Ruben Jaramillo Vacio from the Laboratorio de Pruebas a Equipos y Materiales from the Comisión Federal de Electricidad (LAPEM-CFE), for many useful information on the definition of domain model of partial discharges.
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