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Research Papers

A Neural Network Based Sensor Validation Scheme for Heavy-Duty Diesel Engines

[+] Author and Article Information
Giampiero Campa1

Department of Aerospace Engineering, West Virginia University, Morgantown, WV 26506-6106campa@cemr.wvu.edu

Manoharan Thiagarajan, Mohan Krishnamurty, Marcello R. Napolitano

Department of Aerospace Engineering, West Virginia University, Morgantown, WV 26506-6106

Mridul Gautam1

Department of Aerospace Engineering, West Virginia University, Morgantown, WV 26506-6106mridul.gautam@mail.wvu.edu

1

Corresponding authors.

J. Dyn. Sys., Meas., Control 130(2), 021008 (Feb 29, 2008) (10 pages) doi:10.1115/1.2837314 History: Received February 22, 2006; Revised July 17, 2007; Published February 29, 2008

This paper presents the design of a complete sensor fault detection, isolation, and accommodation (SFDIA) scheme for heavy-duty diesel engines without physical redundancy in the sensor capabilities. The analytical redundancy in the available measurements is exploited by two different banks of neural approximators that are used for the identification of the nonlinear input/output relationships of the engine system. The first set of approximators is used to evaluate the residual signals needed for fault isolation. The second set is used—following the failure detection and isolation—to provide a replacement for the signal originating from the faulty sensor. The SFDIA scheme is explained with details, and its performance is evaluated through a set of simulations in which failures are injected on measured signals. The experimental data from this study have been acquired using a test vehicle appositely instrumented to measure several engine parameters. The measurements were performed on a specific set of routes, which included a combination of highway and city driving patterns.

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Copyright © 2008 by American Society of Mechanical Engineers
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Figures

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Figure 4

Measured versus predicted emissions: validation data with time in seconds

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Figure 5

SFDIA scheme: upper level

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Figure 6

Sensor failure block

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Figure 8

Failure on the MAT sensor at t=2000s: MAT signals in °F and time in seconds

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Figure 10

Failure on the MAT sensor at t=2000s: slow residuals in °F and time in seconds

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Figure 2

Approximators structure (linear+RBF MRAN networks)

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Figure 3

Measured versus predicted emissions: training data with time in seconds

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Figure 1

Schematic of mobile emission measurement system

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Figure 7

Main SFDIA logic

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Figure 9

Failure on the MAT sensor at t=2000s: MAT residuals in °F and time in seconds

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Figure 11

Failure on the rpm sensor at t=1000s: rpm signals with time in seconds

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Figure 12

Failure on the rpm sensor at t=1000s: rpm residuals with time in seconds

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Figure 13

Failure on the rpm sensor at t=1000s: slow residuals with time in seconds

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