An Intelligent Sensor Validation and Fault Diagnostic Technique for Diesel Engines

1999 ◽  
Vol 123 (1) ◽  
pp. 141-144 ◽  
Author(s):  
Ehsan Mesbahi
Keyword(s):  
Fault Diagnosis ◽  
Confidence Level ◽  
Diagnostic Technique ◽  
Operating Conditions ◽  
Sensor Reading ◽  
Intelligent Sensor ◽  
Sensor Validation ◽  
Wide Range ◽  
On Line ◽  
Artificial Neural Network Ann

Abstract An intelligent sensor validation and on-line fault diagnosis technique for a 6 cylinder turbocharged diesel engine is proposed and studied. A single auto-associative 3-layer Artificial Neural Network (ANN), is trained to examine the accuracy of the measured data and allocate a confidence level to each signal. The same ANN is used to recover the missing or faulty data with a close approximation. For on-line fault detection a feed-forward ANN is trained to classify and consequently recognize faulty and healthy behavior of the engine for a wide range of operating conditions. The proposed technique is also equipped with an on-line learning mechanism, which is activated when the confidence level in predicted fault is poor. It is hoped that a feasible, practical, and reliable sensor reading, as well as highly accurate fault diagnosis system, would be achieved.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Fuzzy model-based condition monitoring and fault diagnosis of a diesel engine cooling system

2002 ◽  
Vol 216 (3) ◽  
pp. 215-224 ◽  
Author(s):  
J A Twiddle ◽  
N B Jones
Keyword(s):  
Fault Diagnosis ◽  
Diesel Engine ◽  
Condition Monitoring ◽  
Cooling System ◽  
Fuzzy Model ◽  
Operating Conditions ◽  
Experimental Simulation ◽  
Model Based ◽  
Potential System ◽  
On Line

This paper describes a fuzzy model-based diagnostic system and its application to the cooling system of a diesel engine. The aim is to develop generic cost-effective knowledge-based techniques for condition monitoring and fault diagnosis of engine systems. A number of fuzzy systems have been developed to model the cooling system components. Residuals are generated on line by comparison of measured data with model outputs. The residuals are then analysed on line and classified into a number of fuzzy classes symptomatic of potential system conditions. A fuzzy rule-based system is designed to infer a number of typical fault conditions from the estimated state of the valve and patterns in the residual classes. The ability to diagnose certain faults in the system depends on the state of the thermostatic valve. The diagnostic systems have been tested with data obtained by experimental simulation of a number of target fault conditions on a diesel generator set test bed. In five test cases for separate cooling system operating conditions, the diagnostic system's successful diagnosis rate ranged between 73 and 97.7 per cent of the test data.

scholarly journals GE ENERGY Jenbacher gas engine technology – preventive maintenance by means of highly sophisticated control systems

2006 ◽  
Vol 125 (2) ◽  
pp. 82-86
Author(s):  
Thomas ELSENBRUCH
Keyword(s):  
Control Systems ◽  
Data Transmission ◽  
Operating Conditions ◽  
Engine Operation ◽  
Actual Operating ◽  
Wide Range ◽  
Control Concept ◽  
On Line ◽  
Optimal Adjustment ◽  
And Control

Data collection and control concept of Jenbacher gas engines has been presented in the paper. Internet data transmission allow on-line control of the engine operation, early detection of defects and optimal adjustment to engine actual operating conditions. The system offers both customers and GE Jenbacher maintenance staff a wide range of functionalities for commissioning, monitoring and maintaining installations and for diagnostic purposes.

Electrocoagulation of Textile Effluent: RSM and ANN Modeling

2009 ◽  
Vol 7 (1) ◽  
Author(s):  
A. Arul Murugan ◽  
Thilakavathi Ramamurthy ◽  
Bala Subramanian ◽  
C. Srinivasa Kannan ◽  
Madhavi Ganesan
Keyword(s):  
Electrochemical Reactor ◽  
Operating Conditions ◽  
Cod Removal ◽  
Textile Effluent ◽  
Statistical Tool ◽  
Wide Range ◽  
Artificial Neural Network Ann ◽  
Electro Coagulation ◽  
Level Of Importance ◽  
Acid Blue 113

This investigation attempts to study electrocoagulation of Acid Blue 113 textile effluent in a batch electrochemical reactor covering wide range in the operating conditions. Experiments were designed using statistical tool of Response Surface Method (RSM) to analyze the combined effect of operating parameters on the efficiency of electro coagulation process. The level of importance of the experimental parameters on the percentage COD removal has been determined by using analysis of variance (ANOVA). Further, the percentage COD removal was also modeled using artificial neural network (ANN).

Using Artificial Neural Network (ANN) for Manipulating Energy Gain of Nansulate Coating

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Hadi Salehi ◽  
Mosayyeb Amiri ◽  
Morteza Esfandyari
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Artificial Neural Network ◽  
Energy Gain ◽  
Operating Conditions ◽  
Maximum Relative Error ◽  
Ann Model ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this work, an extensive experimental data of Nansulate coating from NanoTechInc were applied to develop an artificial neural network (ANN) model. The Levenberg–Marquart algorithm has been used in network training to predict and calculate the energy gain and energy saving of Nansulate coating. By comparing the obtained results from ANN model with experimental data, it was observed that there is more qualitative and quantitative agreement between ANN model values and experimental data results. Furthermore, the developed ANN model shows more accurate prediction over a wide range of operating conditions. Also, maximum relative error of 3% was observed by comparison of experimental and ANN simulation results.

How Bulk Modulus Errors Can Affect Leak Detection

1996 ◽  
Author(s):  
Henry H. Rachford ◽  
Andrew Wike
Keyword(s):  
Bulk Modulus ◽  
Measurement Data ◽  
Leak Detection ◽  
Operating Conditions ◽  
Time Data ◽  
Transient Model ◽  
Property Data ◽  
Wide Range ◽  
Fluid Properties ◽  
On Line

Liquid pipeline operators look to leak detection systems to provide continuous surveillance of their pipelines across a wide range of operating conditions; this is particularly the case for batch pipelines. Operators frequently anticipate that on-line transient modeling systems can satisfy this requirement, which they can, but have little exposure to the on-line measurement data requirements of such systems. There can be a mistaken focus on improving the quality of the real-time data normally available to facilitate pipeline operations, without due regard to providing the measurement data that the model needs. Pipeline operators are normally not concerned with a detailed characterization of fluid properties, other than in the most general sense regarding the susceptibility of adjacent fluids to mix at their interface. This paper illustrates how the lack of reliable fluid property data (specifically, bulk modulus data) can substantially impede the effectiveness of a transient model charged with the task of leak detection.

scholarly journals Regime identification of slurry transport in pipelines: A novel modelling approach using ANN & differential evolution

2010 ◽  
Vol 16 (4) ◽  
pp. 329-343 ◽  
Author(s):  
Sandip Lahiri ◽  
K.C. Ghanta
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Differential Evolution ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Slurry Transport ◽  
Pipeline Design

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and differential evolution technique (ANN - DE) for efficient tuning of ANN Meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN - DE method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters. .

Multifault Diagnosis of Induction Motor at Intermediate Operating Conditions Using Wavelet Packet Transform and Support Vector Machine

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Purushottam Gangsar ◽  
Rajiv Tiwari
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Time Series Data ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Operating Conditions ◽  
Series Data ◽  
Support Vector ◽  
Wide Range ◽  
The Impact

This paper proposes advancement in the fault diagnosis of induction motors (IMs) based on the wavelet packet transform (WPT) and the support vector machine (SVM). The aim of this work is to develop and perform the fault diagnosis of IMs at intermediate operating conditions (i.e., the speed and the load) to take care of situations where the data are limited or difficult to obtain at required speeds and loads. In order to check the capability of proposed fault diagnosis, ten different IM fault (mechanical and electrical) conditions are considered simultaneously. In order to obtain the useful information from raw time series data that can characterize each of the fault classes at various operating conditions, the wavelet packet is applied to decompose the data of vibration and current signals from the experimental test rig. Fault features are then obtained using the decomposed data and further used for the diagnosis. In this work, five different wavelet functions (i.e., the Haar, Daubechies, Symlet, Coiflet, and Discrete Meyer) are considered in order to analyze the impact of different wavelets on the IM fault diagnosis. The proposed fault diagnosis has been initially attempted for the same speed and load cases and then extended innovatively to the intermediate speed and load cases. In order to check the robustness of the proposed methodology, the diagnosis is performed for a wide range of motor operating conditions. The results show the feasibility of the proposed fault diagnosis for the successful detection and isolation of various faults of IM, even with limited data or information at some motor operating conditions.

scholarly journals Non-contact incipient fault diagnosis method of fixed-axis gearbox based on CEEMDAN

2017 ◽  
Vol 4 (8) ◽  
pp. 170616 ◽  
Author(s):  
Vanraj ◽  
S. S. Dhami ◽  
B. S. Pabla
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Operating Conditions ◽  
Intrinsic Mode Functions ◽  
Statistical Parameters ◽  
Residual Noise ◽  
Mode Decomposition ◽  
Wide Range ◽  
Fixed Axis

Gearbox plays most essential role in the modern machinery for transmitting the required torque along with motion and contributes to wide range of applications. Any failure in gearbox components affects the productivity and efficiency of the system. Most machine breakdowns related to gears are a result of improper operating conditions and loading, hence lead to failure of the whole mechanism. Ensemble Empirical Mode Decomposition (EEMD) comprises advancement and valuable addition in Empirical Mode Decomposition (EMD) and has been widely used in fault detection of rotating machines. However, intrinsic mode functions (IMFs) produced by EEMD often carry the residual noise. Also, the produced IMFs are different in number due to addition of white Gaussian noise, which leads to final averaging problem. To alleviate these drawbacks, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was previously presented. This paper describes and presents the implementation of CEEMDAN for fault diagnosis of simulated local defects using sound signals in a fixed-axis gearbox. Statistical parameters are extracted from decomposed sound signals for different simulated faults. Results show the effectiveness of CEEMDAN over EEMD in order to obtain more accurate IMFs and fault severity.

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