Simulated Failure Conditions Research Articles

Diagnosis of a hydrogen-fueled 1-kW PEMFC system based on exergy analysis

In this study, a model based diagnosis of a hydrogen-fueled 1-kW proton exchange membrane fuel cell (PEMFC) system was conducted based on exergy analysis to identify faulty components. Faulty components of balance of plant (BOP) were experimentally simulated by reducing the rpm signals of the pump, fan, and air and fuel blowers; reducing the output signals of the air and fuel flow meters; increasing the output signal of the temperature sensor; opening the valve between the air blower and the air flow meter; and blocking the heat exchange area during the operation of the PEMFC system. The irreversibility rate, malfunction (MF) and dysfunction (DYS) of each component were calculated for the case under normal condition and simulated failure conditions using the observed data. The residuals of the MF and relative malfunction (RMF) between the normal and failure conditions were the fault indicators used to identify the faulty components in the system. The proposed diagnosis method employing both the MF and the RMF was found to be not only simple but also effective.

Application of the CANARY event detection software for real-time performance monitoring of decentralized water reuse systems.

Real-time monitoring of water reuse systems ensures the production of high quality water to protect human health at the point-of-use. In this study, several online real-time sensors were utilized to monitor effluent from a wastewater fed laboratory-scale membrane bioreactor (MBR) under natural and simulated failure conditions. These simulated failures included adding reactor mixed liquor to emulate a membrane breach, and spiking MS2 bacteriophage into the reactor to create a high viral load, which might be observed during an outbreak. The CANARY event detection software was used to analyze sensor data and report changes in water quality that might be indicative of poor system behavior. During simulated failure conditions, CANARY reported 20 alarms, accurately detecting each failure. During natural operating conditions, 219 alarms were produced and 189 were attributed to known events (e.g., system and sensor maintenance). The remaining alarms (23) during natural operating conditions were considered to have an unknown cause. However, 13 of those had signal deviations similar to known events, but could not be definitively linked to a source. The results of this study suggest that real-time monitoring in conjunction with CANARY analysis may be useful as an early warning system for monitoring the effluent of water reuse systems, and may help to quickly identify treatment malfunctions or other abnormal conditions.

Augmentation of an Intelligent Flight Control System for a Simulated C-17 Aircraft

This paper examines a neural-adaptive flight control system augmented with linear programming theory and adaptive critic techniques for a simulated C-17 aircraft. The baseline Intelligent Flight Control (IFC) system is composed of a neural network based direct adaptive control approach for applying alternate sources of control authority in the presence of damage or failures in order to achieve desired flight control performance. Neural networks are used to provide consistent handling qualities across flight conditions, adapt to changes in aircraft dynamics and make the controller easy to apply when implemented on different aircraft. In this study, IFC has been augmented with linear programming (LP) theory and adaptive critic technologies. LP is used to optimally allocate requested control deflections and the adaptive critic modifies the parameters of the aircraft reference model for consistent handling qualities. Full-motion piloted simulation studies were performed on a Boeing C-17. Subjects included NASA and Air Force pilots. Results, including subjective pilot ratings and time response characteristics of the system, demonstrate the potential for improving handing qualities and significantly increased survivability rates under various simulated failure conditions.