Fuzzy fault tree analysis for controlling robot-related accidents involving humans in industrial plants: a case study

Abstract

PurposeIn recent years, the application of robots in different industrial sectors such as nuclear power generation, construction, automobile, firefighting and medicine, etc. is increasing day by day. In large industrial plants generally humans and robots work together to accomplish several tasks and lead to the problem of safety and reliability because any malfunction event of robots may cause human injury or even death. To access the reliability of a robot, sufficient amount of failure data is required which is sometimes very difficult to collect due to rare events of any robot failures. Also, different types of their failure pattern increase the difficulty which finally leads to the problem of uncertainty. To overcome these difficulties, this paper presents a case study by assessing fuzzy fault tree analysis (FFTA) to control robot-related accidents to provide safe working environment to human beings in any industrial plant.Design/methodology/approachPresented FFTA method uses different fuzzy membership functions to quantify different uncertainty factors and applies alpha-cut coupled weakest t-norm (Tω) based approximate fuzzy arithmetic operations to obtain fuzzy failure probability of robot-human interaction fault event which is the main contribution of the paper.FindingsThe result obtained from presented FFTA method is compared with other listing approaches. Critical basic events are also ranked using V-index for making suitable action plan to control robot-related accidents. Study indicates that the presented FFTA is a good alternative method to analyze fault in robot-human interaction for providing safe working environment in an industrial plant.Originality/valueExisting fuzzy reliability assessment techniques designed for robots mainly use triangular fuzzy numbers (TFNs), triangle vague sets (TVS) or triangle intuitionistic fuzzy sets (IFS) to quantify data uncertainty. Present study overcomes this shortcoming and generalizes the idea of fuzzy reliability assessment for robots by adopting different IFS to control robot-related accidents to provide safe working environment to human. This is the main contribution of the paper.

Fault diagnosis of robot joint based on BP neural network

“…After completed the establishment of the fault database, we need to perform feature extraction on the fault data and the normal data, so as to realize the judgment of the fault state. Komal researched on robot fault diagnosis by establishing a fuzzy fault tree for the robot, the fault tree is an inverted tree-like logic causality diagram that connects events with logic gate symbols, fault tree analysis (FTA) is a top-down deductive failure analysis method to analyze undesired states in the system [8]. Xu et al determined the motion state of the robot by analyzing the motion signal of the robot arm and comparing the velocity curve and threshold of the robot end joint, and then carried out the fault diagnosis of the robot arm joint [9].…”