A Bayesian based Intelligent Troubleshooting System

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ScienceDirect

Procedia Computer Science 200 (2022) 602–610

3rd International Conference on Industry 4.0 and Smart Manufacturing

A Bayesian based Intelligent Troubleshooting System

I Yung∗, Federico D’ambrosio, Alissa Zaccaria, Fabio Floreani

Consorzio Intellimech, Via Stezzano 87, Bergamo 24126, Italy

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Abstract

Troubleshooting systems can bring different benefits in assets management, particularly for service operations, facilitating the di- agnosis of problems and faulty components identification. However, these systems are commonly based on rigid computation logic unable to handle uncertainties. In this work, a knowledge-based system exploiting the Bayesian theorem was developed and applied in a troubleshooting tool that relies on human-machine interaction. The required knowledge and the algorithm were analyzed and tested to ensure robustness and self-learning capabilities. Subsequently, the system was implemented in an industrial environment, specifically from a crane manufacturing company. The algorithm is robust to errors and provides the possibility of not answering some questions. However, the system performance is highly dependent on the questions, both in terms of quantity (adequate num- ber compared to possible failures) and quality (effective to discriminate among failures). Indeed, this work shows how the system knowledge enhancement by introducing additional questions can significantly improve the troubleshooting performance. Future developments may involve user-friendliness enhancement and self-learning implementation to add and update questions over time.

© 2022 The Authors. Published by Elsevier B.V.

This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Peer-review under responsibility of the scientific committee of the 3rd International Conference on Industry 4.0 and Smart Manufacturing

Keywords: Knowledge based system; Bayesian theorem; troubleshooting system; human support; self-learning

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1. Introduction

The potentialities of expert systems in assisting the human workforce have been investigated over the years in different fields. The first computer-based consultation system, Mycin, was proposed in the medical field. Mycin aimed to support physicians in bacterial infections diagnosis, allowing the selection of the most suitable therapy [1]. In the manufacturing field, computer-aided troubleshooting systems have been deemed suitable to overcome the scarcity of experienced troubleshooters [2].

Highly skilled troubleshooters are commonly trained on the job over a long period. Indeed, it is very challeng- ing for companies to replace such a skilled workforce. Moreover, the evolution of machines complexity introduces additional challenges to the troubleshooting process. In this regard, several computer-based troubleshooting systems

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∗ Corresponding author. Tel.: +39-035-069-0366.

E-mail address: i.yung@intellimech.it

1877-0509 © 2022 The Authors. Published by Elsevier B.V.

This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)

Peer-review under responsibility of the scientific committee of the 3rd International Conference on Industry 4.0 and Smart Manufacturing 10.1016/j.procs.2022.01.258

were developed based on the knowledge collected by experts specialized in the identification of machine failures (e.g., [3]). However, these systems are commonly based on rigid computation logic and unable to handle the uncertainties (different from what human is typically capable of).

Machine learning algorithms, which represent the basis of modern approaches to artificial intelligence, can be exploited to solve this problem. Indeed, these algorithms, based on statistical rules, can compensate for typical uncer- tainties and human errors. The objective of the present work is to investigate and exploit the possible algorithms behind a well-known artificial intelligence game, Akinator, for a robust troubleshooting system implementation, tolerant to mistakes and uncertain answers.

The game Akinator is able to guess a character, either a real or an imaginary person, after asking the player several closed-ended questions even in the presence of some wrong answers. Besides its robustness, other significant features of Akinator include the following:

• Available answers are limited: yes, no, don’t know, probably, probably not. The last three possible answers might allow the system to handle uncertainties;
• The questions order is not randomly selected. The game mostly starts with general questions and ends with specific ones;
• There are several repetitive questions. Namely, two different questions obtaining the same information can exist (e.g., a question asking the character’s gender is female and another question asking if the character’s gender is male).

If the system is not able to identify the character, a list of possible characters is presented, allowing the user to select the one they are referring to. If the character is not present in the list, the user is asked to introduce a new one with a brief description. The character in Akinator refers to a person which the system should identify among all the possible candidates. The candidates can basically be any form of entity that the system is expected to identify. that can also be in the form of objects, numbers or failures in the case of troubleshooting.

Akinator presents advantageous features that could be exploited for troubleshooting purposes, such as simple an- swers, question order, learning capability and robustness to wrong answers. In this paper, the characteristics of several similar systems are first illustrated (section 2). Then, the most promising algorithm is selected and applied to a test dataset (section 3) before its validation on an industrial dataset (section 4). Finally, the simulation results are presented (section 5), followed by the conclusions and future developments (section 6).

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