Nonlinear PCA With the Local Approach for Diesel Engine Fault Detection and Diagnosis

2008 ◽  
Vol 16 (1) ◽  
pp. 122-129 ◽  
Author(s):  
Xun Wang ◽  
Uwe Kruger ◽  
George W. Irwin ◽  
Geoff McCullough ◽  
Neil McDowell
2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
S. H. Gawande ◽  
L. G. Navale ◽  
M. R. Nandgaonkar ◽  
D. S. Butala ◽  
S. Kunamalla

Early fault detection and diagnosis for medium-speed diesel engines are important to ensure reliable operation throughout the course of their service. This work presents an investigation of the diesel engine combustion-related fault detection capability of crankshaft torsional vibrations. Proposed methodology state the way of early fault detection in the operating six-cylinder diesel engine. The model of six cylinders DI Diesel engine is developed appropriately. As per the earlier work by the same author the torsional vibration amplitudes are used to superimpose the mass and gas torque. Further mass and gas torque analysis is used to detect fault in the operating engine. The DFT of the measured crankshaft’s speed, under steady-state operating conditions at constant load shows significant variation of the amplitude of the lowest major harmonic order. This is valid both for uniform operating and faulty conditions and the lowest harmonic orders may be used to correlate its amplitude to the gas pressure torque and mass torque for a given engine. The amplitudes of the lowest harmonic orders (0.5, 1, and 1.5) of the gas pressure torque and mass torque are used to map the fault. A method capable to detect faulty cylinder of operating Kirloskar diesel engine of SL90 Engine-SL8800TA type is developed, based on the phases of the lowest three harmonic orders.


Author(s):  
Abdulhalim Maulud ◽  
José Romagnoli

This paper presents fault detection and diagnosis methodology for batch/semi-batch processes using a multi-way orthogonal nonlinear PCA approach. In this work, a sequential extracting process of linear and nonlinear correlations from process data is performed. The approach reduces the complexity of the nonlinear PCA model structure, which dramatically improves the model generalization. An orthogonal nonlinear PCA procedure is incorporated to capture the nonlinear characteristics with a minimum number of principal components. A trajectory-boundary-limit crossing point discriminant analysis is proposed to diagnose the process faults. A two-step discriminant analysis is also incorporated to improve the diagnostic performance in the case of isotropically distributed trajectories. The validity of the proposed strategy is demonstrated by the application to an emulsion copolymerization of styrene/MMA semi-batch process.


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