Fault severity estimation in a vehicle cooling system

Abstract

On-time water pump malfunction, thermostat sticking, fan defect, and radiator clogging severity estimation in a vehicle cooling system prevents system breakdown and saves repair costs and service time. In this respect, to minimize the required number of engine variable measurements, the engine body temperature, T b is introduced as a single driving force of the thermal system and a modified adaptive second order sliding mode (SOSM) fault observer is utilized to estimate the (single case) faults despite the system nonlinear uncertainty. The faults are applied and the estimates are obtained within the defined temperature ranges. The results of extensive simulations indicate that the algorithm works well in assessing the occurred damages to the uncertain system. KEYWORDS adaptive second order sliding mode, automobile cooling system, fault detection, observer 2 | AUTOMOBILE COOLING SYSTEM MODEL The engine heat, not released through the exhaust, is dissipated by the cooling system. Since the engine body is massive with high thermal conductivity, it is approximately modeled by a variable temperature source, T b. This possibility simplifies the model and lets a simpler estimation algorithm to be built.

Application of Technology of Additive Manufacturing in Radiators and Heat Exchangers

“…Additive manufacturing (often abbreviated to AM), also named 3D printing, adopts the stacking method of layer upon layer via point → line → surface → body, which releases the degree of design freedom and provides an implementation way for new structural radiators and heat exchangers [2]. Therefore, many enterprises and institutions have conducted additive manufacturing research to improve the performance of radiators and heat exchangers and achieved some innovative results [3]. This paper expounds on the application of additive manufacturing in radiators and heat exchangers.…”

Accurate fault detection during power swing: compensated line study

“…Generally, there are two categories of techniques for fault detection/estimation, namely, model-based and signal-based approaches, each with numerous variations of algorithms. When the system model is partially known, the model-based procedures are favorite; such as those detailed in Nemati and Seyedtabaii (2019) and Shams and Seyedtabaii (2020). On the other hand, where the system is large and complex the signal-based methods are more attractive.…”

Fault detection and faulty phase(s) identification in TCSC compensated transmission lines

“…Fault detection is classified into model and signal-based approaches. Example of a model-based method has been presented in [3]. In cases that establishing model is difficult; signal-based methods are the available choices.…”