In this paper, a novel chatbot for risk reduction as an aid during machinery design is presented. The general workflow of the chatbot involves the identification of the hazard described by the user using a neural network model followed by an interactive dialog based conversation, in which the risk reduction measures are outlined. A prototype implementation of the chatbot presents the steps to generate and pre-process the training data for Artificial Intelligence (AI) based models. Different neural network models are trained and evaluated for the proposed risk reduction chatbot. A comparative study is presented by employing an in-depth qualitative and quantitative evaluation. The work presented in this paper shows significant promise in ensuring safety awareness, thereby aiding in implementing functional safety in the early stages of machinery design and development.

The use of Artificial Intelligence (AI) in machinery functional safety can enhance efficiency and accuracy by automating tasks previously carried out by humans. This paper presents an experimental evaluation of Neural Network (NN) models for hazard identification in machinery functional safety. The systematic study includes own implementations of NN models using open source building blocks and the use of an open source conversational AI framework with various pipeline configurations. The paper provides a comparative analysis of the qualitative and quantitative parameters for the models and configurations.

Safety awareness is extremely important when Artificial Intelligence (AI)/Machine Learning (ML) is introduced in the functional safety domain. This paper shows a way to bring the characteristics of the ML development process as well as their particular characteristics into a consensus of the engineering process of functional safety and its reliability requirements.

Standards such as ISO 13849 and ISO 12100 enable users to model safety related control elements with safety functions, according to a specified architecture and required performance level. In this direction, a novel Artificial Intelligence (AI)-based assistant is introduced in this paper to aid in determining the required performance level parameter by indulging the user in a dialog-based conversation regarding hazard scenarios. This will help inexperienced machinery safety personnel (e.g. mechanical engineer) to get an overview of the safety engineering aspects, before consulting with safety experts for risk assessment.