Dynamic fault detection and isolation for automotive engine air path by independent neural network model

Yu, Dingli; Hamad, Adnan; Gomm, J.B.; Sangha, Mahavir S

doi:10.1177/1468087412461267

Cited by 12 publications

(4 citation statements)

References 18 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A survey on diagnosis methods for automotive engines is presented in Mohammadpour et al 22 Yu et al 23 investigated the dynamic fault detection and isolation for automotive engines by employing neural network models. Tse and Tse 24 focused on the diagnosis of the combustion-related faults (defective spark plug, oil leakage, defective valves, cylinder wear) in automotive engines by the use of low-cost sensors for the instantaneous angular speed method.…”

Section: Introductionmentioning

confidence: 99%

A novel methodology for marine dual fuel engines sensors diagnostics and health management

Stoumpos

Theotokatos

2021

International Journal of Engine Research

View full text Add to dashboard Cite

The sensors abnormalities, faults, failure detection and diagnosis for marine engines are considered crucial for ensuring the engine safe and smooth operation. The development of such system(s) is typically based on the manufacturers experience on sensors and actuators faults and failure events. This study aims to introduce a novel methodology for the sensors diagnostics and health management in marine dual fuel engines by employing a combination of thermodynamic, functional control and data-driven models. The concept of an Engine Diagnostics System (EDS) is developed to provide intelligent engine monitoring, advanced sensors’ faults detection as well as timely and profound corrective actions. This system employs a neural networks (NN) Data-Driven (DD) model along with appropriate logic controls. The DD model is set up based on the derived steady state data from a thermodynamic model of high fidelity and is capable of real-time prediction of the engine health condition behaviour. The concept of a novel Unified Digital System (UDS) is proposed that combines the engine’s existing control and diagnostic systems with the EDS. The functionality of the UDS system is validated by employing a digital twin of the considered marine dual fuel engine by investigating scenarios for assessing the engine performance that entail abnormalities in the engine’s speed and boost pressure sensors. The simulation results demonstrate that the developed UDS is capable of sufficiently capturing the engine’s sensors abnormalities and applying appropriate corrective actions to restore the engine operation in its original state. This study benefits the development future systems facilitating the engines condition assessment and self-correction of the engine sensors’ abnormalities, which will be required for smart and autonomous shipping.

show abstract

Section: Introductionmentioning

confidence: 99%

A novel methodology for marine dual fuel engines sensors diagnostics and health management

Stoumpos

Theotokatos

2021

International Journal of Engine Research

View full text Add to dashboard Cite

show abstract

“…Another type of fault diagnosis system uses a nonlinear dynamic model to predict system output. The model can be constructed with fuzzy logic or neural networks (Kamal et al 2014;Yu et al 2014). In recent years, a nonlinear observer with an on-line estimator method (Zhang et al 2002;Ferrari et al 2009;Polycarpou et al 2004) attracted much attention of researchers.…”

Section: Introductionmentioning

confidence: 99%

Fault tolerant control for nonlinear systems using sliding mode and adaptive neural network estimator

Shi

et al. 2019

Soft Comput

View full text Add to dashboard Cite

This paper proposes a new fault tolerant control scheme for a class of nonlinear systems including robotic systems and aeronautical systems. In this method, a sliding mode control is applied to maintain system stability under the post-fault dynamics. A neural network is used as on-line estimator to reconstruct the change rate of the fault and compensate for the impact of the fault on the system performance. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimator is guaranteed to converge to the fault change rate, while the entire closed-loop system stability and tracking control is guaranteed. Compared with the existing methods, the proposed method achieved fault tolerant control for time-varying fault, rather than just constant fault. This greatly expands the industrial applications of the developed method to enhance system reliability. The main contribution and novelty of the developed method is that the system stability is guaranteed and the fault estimation is also guaranteed for convergence when the system subject to a time-varying fault. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results demonstrated that the post-fault is stable and the performance is maintained.

show abstract

“…The model can be constructed with fuzzy logic or neural networks. 6,7 A function of the modelling error was then used as the detection residual. These models are not adaptive and therefore the system uncertainties will significantly affect the detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

A new fault diagnosis and fault-tolerant control method for mechanical and aeronautical systems with neural estimators

Shi

Tian

et al. 2019

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

A new method of fault detection and fault-tolerant control is proposed in this article for mechanical systems and aeronautical systems. The faults to be estimated and diagnosed are malfunctions that occurred within the control loops of the systems, rather than some static faults, such as gearbox fault, component cracks, and so on. In the proposed method, two neural networks are used as online estimators, the fault will be accurately estimated when the estimators are adapted online with the post-fault dynamic information. Furthermore, the estimated values of faults are used to compensate for the impact of the faults, so that the stability and performance of the system with the faults are maintained until the faulty components to be repaired. The sliding mode control is used to maintain system stability under the post-fault dynamics. The control law and the neural network learning algorithms are derived using the Lyapunov method, so that the neural estimators are guaranteed to converge to the fault to be diagnosed, while the entire closed-loop system stability is guaranteed with all variables bounded. The main contribution of this article to the knowledge in this field is that the proposed method can not only diagnose and tolerant with constant fault but also diagnose and tolerant with the time-varying faults. This is very important because most faults occurred in industrial systems are time varying in nature. A simulation example is used to demonstrate the design procedure and the effectiveness of the method. The simulation results are compared with the two existing methods that can cope with constant faults only, and the superiority is demonstrated.

show abstract

Dynamic fault detection and isolation for automotive engine air path by independent neural network model

Cited by 12 publications

References 18 publications

A novel methodology for marine dual fuel engines sensors diagnostics and health management

A novel methodology for marine dual fuel engines sensors diagnostics and health management

Fault tolerant control for nonlinear systems using sliding mode and adaptive neural network estimator

A new fault diagnosis and fault-tolerant control method for mechanical and aeronautical systems with neural estimators

Contact Info

Product

Resources

About