scholarly journals Residual generator fuzzy identification for automotive diesel engine fault diagnosis

2013 ◽  
Vol 23 (2) ◽  
pp. 419-438 ◽  
Author(s):  
Silvio Simani
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Control Strategies ◽  
Direct Consequence ◽  
Fault Detection And Isolation ◽  
Fuzzy Identification ◽  
Overall Performance ◽  
Residual Generator ◽  
Residual Generation

Safety in dynamic processes is a concern of rising importance, especially if people would be endangered by serious system failure. Moreover, as the control devices which are now exploited to improve the overall performance of processes include both sophisticated control strategies and complex hardware (input-output sensors, actuators, components and processing units), there is an increased probability of faults. As a direct consequence of this, automatic supervision systems should be taken into account to diagnose malfunctions as early as possible. One of the most promising methods for solving this problem relies on the analytical redundancy approach, in which residual signals are generated. If a fault occurs, these residual signals are used to diagnose the malfunction. This paper is focused on fuzzy identification oriented to the design of a bank of fuzzy estimators for fault detection and isolation. The problem is treated in its different aspects covering the model structure, the parameter identification method, the residual generation technique, and the fault diagnosis strategy. The case study of a real diesel engine is considered in order to demonstrate the effectiveness the proposed methodology.

scholarly journals Residual Generator Fuzzy Identification for Wind TurbineBenchmark Fault Diagnosis

Machines ◽  
2014 ◽  
Vol 2 (4) ◽  
pp. 275-298 ◽  
Author(s):  
Silvio Simani ◽  
Saverio Farsoni ◽  
Paolo Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Identification ◽  
Residual Generator

Residual Generator Fuzzy Identification for Wind Farm Fault Diagnosis

2014 ◽  
Vol 47 (3) ◽  
pp. 4310-4315 ◽  
Author(s):  
S. Simani ◽  
S. Farsoni ◽  
P. Castaldi
Keyword(s):  
Fault Diagnosis ◽  
Wind Farm ◽  
Fuzzy Identification ◽  
Residual Generator

Fault Diagnosis With a Model-Based Recurrent Neural Network

2000 ◽  
Author(s):  
Chengyu Gan ◽  
Kourosh Danai
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Recurrent Neural Network ◽  
State Space Model ◽  
Black Box ◽  
Fault Detection And Isolation ◽  
Model Based ◽  
Starting Point ◽  
Residual Generator ◽  
Structure Assignment

Abstract The utility of a model-based recurrent neural network (MBRNN) is demonstrated in fault diagnosis. The MBRNN can be formatted according to a state-space model. Therefore, it can use model-based fault detection and isolation (FDI) solutions as a starting point, and improve them via training by adapting them to plant nonlinearities. In this paper, the application of MBRNN to the IFAC Benchmark Problem is explored and its performance is compared with ‘black box’ neural network solutions. For this problem, the MBRNN is formulated according to the Eigen-Structure Assignment (ESA) residual generator developed by Jorgensen et al. [1]. The results indicate that the MBRNN provides better results than ‘black box’ neural networks, and that with training it can perform better than the ESA residual generator.

scholarly journals Structural Methodologies for Distributed Fault Detection and Isolation

2019 ◽  
Vol 9 (7) ◽  
pp. 1286 ◽  
Author(s):  
Hamed Khorasgani ◽  
Gautam Biswas ◽  
Daniel Jung
Keyword(s):  
Fault Detection ◽  
Fault Detection And Isolation ◽  
Aircraft Manufacturing ◽  
Pros And Cons ◽  
Distributed Fault Detection ◽  
Model Equations ◽  
Future Work ◽  
Networking Technology ◽  
Residual Generation

The increasing complexity and size of cyber-physical systems (e.g., aircraft, manufacturing processes, and power generation plants) is making it hard to develop centralized diagnosers that are reliable and efficient. In addition, advances in networking technology, along with the availability of inexpensive sensors and processors, are causing a shift in focus from centralized to more distributed diagnosers. This paper develops two structural approaches for distributed fault detection and isolation. The first method uses redundant equation sets for residual generation, referred to as minimal structurally-over-determined sets, and the second is based on the original model equations. We compare the diagnosis performance of the two algorithms and clarify the pros and cons of each method. A case study is used to demonstrate the two methods, and the results are discussed together with directions for future work.

scholarly journals Neuro-Fuzzy Sensor Fault Diagnosis of an Induction Motor

2011 ◽  
Vol 7 (2) ◽  
pp. 53 ◽  
Author(s):  
M. L. Benloucif
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Fuzzy Neural Network ◽  
Fault Detection And Isolation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Neuro Fuzzy ◽  
Fuzzy Neural ◽  
Residual Generation

 In this paper, a neuro-fuzzy fault diagnosis scheme is presented and its ability to detect and isolate sensor faults in an induction motor is assessed. This fault detection and isolation (FDI) approach relies on a combination of neural modelling and fuzzy logic techniques which can deal effectively with nonlinear dynamics and uncertainties. It is based on a two step neural network procedure: a first neural network is used for residual generation and a second fuzzy neural network performs residual evaluation. Simulation results are given to demonstrate the efficiency of this FDI approach. 

scholarly journals A CASE STUDY OF VIBRATION FAULT DIAGNOSIS APPLIED AT ROLLS-ROYCE T-56 TURBOPROP ENGINE

Aviation ◽  
2020 ◽  
Vol 23 (3) ◽  
pp. 78-82
Author(s):  
Christos Skliros
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Root Cause ◽  
Significant Challenge ◽  
Turboprop Engine

Gas turbine engines include a plethora of rotating modules, and each module consists of numerous components. A component’s mechanical fault can result in excessive engine vibrations. Identification of the root cause of a vibration fault is a significant challenge for both engine manufacturers and operators. This paper presents a case study of vibration fault detection and isolation applied at a Rolls-Royce T-56 turboprop engine. In this paper, the end-to-end fault diagnosis process from starting system faults to the isolation of the engine’s shaft that caused excessive vibrations is described. This work contributes to enhancing the understanding of turboprop engine behaviour under vibration conditions and highlights the merit of combing information from technical logs, maintenance manuals and engineering judgment in successful fault diagnosis.

scholarly journals Framework for Unifying Model-based and Data-driven Fault Diagnosis

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Hamed Khorasgani ◽  
Ahmed Farahat ◽  
Kosta Ristovski ◽  
Chetan Gupta ◽  
Gautam Biswas
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Method ◽  
Fault Detection And Isolation ◽  
The Other ◽  
Data Driven ◽  
Isolate System ◽  
Combined Model ◽  
Model Based ◽  
Abnormal Behaviors

Model-based diagnosis methods rely on a model that defines nominal behavior of a dynamic system to detect abnormal behaviors and isolate faults. On the other hand, data-driven diagnosis algorithms detect and isolate system faults by operating exclusively on system measurements and using very little knowledge about the system. Recently, several researchers have combined model-based diagnosis techniques with datadriven approaches to propose hybrid1solutions for fault diagnosis. Many researchers have proposed methods to integrate specific approaches. In this paper, we demonstrate that data-driven and model-based diagnosis methods follow a similar procedure and can be represented by a general unifying framework. This unifying framework for fault detection and isolation can be used to integrate different methodologies developed by two communities. As a case study, we use the proposed framework to build a crossover solution for fault diagnosis in a wind turbine benchmark. In this case study, we show that it is possible to achieve a better diagnosis performance by using a hybrid method that follows the proposed framework.

scholarly journals Model based fault detection and diagnosis using structured residual approach in a multi-input multi-output system

2007 ◽  
Vol 4 (2) ◽  
pp. 133-145 ◽  
Author(s):  
A. Asokan ◽  
D. Sivakumar
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Test Method ◽  
Fault Detection And Isolation ◽  
Generalized Likelihood Ratio Test ◽  
Ratio Test ◽  
Output System ◽  
Detection And Diagnosis ◽  
Residual Generation

Fault detection and isolation (FDI) is a task to deduce from observed variable of the system if any component is faulty, to locate the faulty components and also to estimate the fault magnitude present in the system. This paper provides a systematic method of fault diagnosis to detect leak in the three-tank process. The proposed scheme makes use of structured residual approach for detection, isolation and estimation of faults acting on the process [1]. This technique includes residual generation and residual evaluation. A literature review showed that the conventional fault diagnosis methods like the ordinary Chisquare (?2) test method, generalized likelihood ratio test have limitations such as the "false alarm" problem. From the results it is inferred that the proposed FDI scheme diagnoses better when compared to other conventional methods.

Case Study on Fault Diagnosis of the Actual Operating Transformer by FRA

2011 ◽  
Vol 131 (1) ◽  
pp. 78-85 ◽  
Author(s):  
Takahiro Sano ◽  
Yoshiharu Ogawa ◽  
Takaaki Shimonosono ◽  
Tadayuki Wada
Keyword(s):  
Fault Diagnosis ◽  
Actual Operating

Pollution based real time control strategies for combined sewer systems

1997 ◽  
Vol 36 (8-9) ◽  
pp. 331-336 ◽  
Author(s):  
Gabriela Weinreich ◽  
Wolfgang Schilling ◽  
Ane Birkely ◽  
Tallak Moland
Keyword(s):  
Real Time ◽  
Control Strategies ◽  
Ammonia Nitrogen ◽  
Simple Extension ◽  
Real Time Control ◽  
Time Control ◽  
Sewer Systems ◽  
Receiving Waters ◽  
Tunnel System

This paper presents results from an application of a newly developed simulation tool for pollution based real time control (PBRTC) of urban drainage systems. The Oslo interceptor tunnel is used as a case study. The paper focuses on the reduction of total phosphorus Ptot and ammonia-nitrogen NH4-N overflow loads into the receiving waters by means of optimized operation of the tunnel system. With PBRTC the total reduction of the Ptot load is 48% and of the NH4-N load 51%. Compared to the volume based RTC scenario the reductions are 11% and 15%, respectively. These further reductions could be achieved with a relatively simple extension of the operation strategy.

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