Model-based Fault Injection Experiments for the Safety Analysis of Exoskeleton System

Abstract

Model-based fault injection methods are widely used for the evaluation of fault tolerance in safety-critical control systems. In this paper, we introduce a new model-based fault injection method implemented as a highlycustomizable Simulink block called FIBlock. It supports the injection of typical faults of essential heterogeneous components of Cyber-Physical Systems, such as sensors, computing hardware, and network. The FIBlock GUI allows the user to select a fault type and configure multiple parameters to tune error magnitude, fault activation time, and fault exposure duration. Additional trigger inputs and outputs of the block enable the modeling of conditional faults. Furthermore, two or more FIBlocks connected with these trigger signals can model chained errors. The proposed fault injection method is demonstrated with a lower-limb EXO-LEGS exoskeleton, an assistive device for the elderly in everyday life. The EXO-LEGS model-based dynamic control is realized in the Simulink environment and allows easy integration of the aforementioned FIBlocks. Exoskeletons, in general, being a complex CPS with multiple sensors and actuators, are prone to hardware and software faults. In the case study, three types of faults were investigated: 1) sensor freeze, 2) stuck-at-0, 3) bit-flip. The fault injection experiments helped to determine faults that have the most significant effects on the overall system reliability and identify the fine line for the critical fault duration after that the controller could no longer mitigate faults.

Property-Based Mutation Testing

“…Mutation mainly relies on alterations in the Simulink model by seeding defects using mutation operators [52]. Researchers have proposed several tools for creating mutants: SIMULTATE [53], MODIFI [54], ErrorSim [55], FIBlock [56], and FIM [34]. We also mention SLforge [19], a tool for automatically generating random valid Simulink models for differential testing.…”

DELFASE: A Deep Learning Method for Fault Space Exploration

“…In addition to Safety Check, we have added FIBlock to the original ACC model to inject faults into the acceleration signal transferred between ACC and the ego car. This block is an instance of the Fault Injection Block introduced in [7] as a MATLAB extension. This extension supports six common types of faults (i.e., stuck-at-value, packet-loss, bias/offset, bitflip, delay and noise), and has the following configuration parameters [7]:…”

“…This block is an instance of the Fault Injection Block introduced in [7] as a MATLAB extension. This extension supports six common types of faults (i.e., stuck-at-value, packet-loss, bias/offset, bitflip, delay and noise), and has the following configuration parameters [7]:…”

“…This model represents the knowledge learned automatically through interaction with the SUT, and helps us locate more critical faults with less effort. Note that, effective exploration of the fault space has also been studied in the past using other techniques such as those that are deterministic [5], [6] or model-based [7], as well as those that are based on evolutionary optimization [8] or reinforcement learning [9].…”