Energy system performance may differ from the expected one during actual operation because of the effects of faults, anomalies, and wear and tear due to normal use. One of the main issues of diagnosis, i.e., the procedure to discover the causes of malfunctions, is to find the way back from measured altered performance to the original cause. Several procedures were proposed in the literature to solve the diagnostic problem, usually based on the comparison between a reference nonmalfunctioning condition and an actual, possibly malfunctioning, condition. A different strategy is suggested in the paper. A direct search of the possible causes of malfunctions is performed by means of an evolutionary algorithm: a component fault is arbitrarily introduced in a model of the healthy system by substituting the reference characteristic curve with an altered one, and the algorithm is used to search for a combination of different kinds of performance modifiers that generates the same measured effects of the actual anomaly. A global and a local approach are proposed and applied to a real test case plant, also in presence of measurement noise. The local approach demonstrates to be more effective in terms of accuracy and computational effort.

1.
Li
,
Y. G.
, 2002, “
Performance-Analysis-Based Gas Turbine Diagnostics: A Review
,”
Proc. Inst. Mech. Eng., Part A
0957-6509,
216
, pp.
363
377
.
2.
Marinai
,
L.
,
Probert
,
D.
, and
Singh
,
R.
, 2004, “
Prospects for Aero Gas-Turbine Diagnostics: A Review
,”
Appl. Energy
0306-2619,
79
, pp.
109
126
.
3.
Volponi
,
A. J.
,
DePold
,
H.
,
Ganguli
,
R.
, and
Daguang
,
C.
, 2003, “
The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
125
, pp.
917
924
.
4.
Ogaji
,
S. O. T.
, and
Singh
,
R.
, 2003, “
Advanced Engine Diagnostics Using Artificial Neural Networks
,”
Appl. Soft Comput.
1568-4946,
3
, pp.
259
271
.
5.
Romessis
,
C.
, and
Mathioudakis
,
K.
, 2006, “
Bayesian Network Approach for Gas Path Fault Diagnosis
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
128
, pp.
64
72
.
6.
Biagetti
,
T.
, and
Sciubba
,
E.
, 2004, “
Automatic Diagnostics and Prognostics of Energy Conversion Processes via Knowledge-Based Systems
,”
Energy
0360-5442,
29
, pp.
2553
2572
.
7.
Ganguli
,
R.
, 2003, “
Application of Fuzzy Logic for Fault Isolation of Jet Engines
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
125
, pp.
617
623
.
8.
Ogaji
,
S. O. T.
,
Marinai
,
L.
,
Sampath
,
S.
,
Singh
,
R.
, and
Probert
,
S. D.
, 2005, “
Gas-Turbine Fault Diagnostics: A Fuzzy-Logic Approach
,”
Appl. Energy
0306-2619,
82
, pp.
81
89
.
9.
Sampath
,
S.
,
Ogaji
,
S.
,
Singh
,
R.
, and
Probert
,
D.
, 2002, “
Engine-Fault Diagnostics: An Optimisation Procedure
,”
Appl. Energy
0306-2619,
73
, pp.
47
70
.
10.
Ogaji
,
S. O. T.
,
Sampath
,
S.
,
Marinai
,
L.
,
Singh
,
R.
, and
Probert
,
S. D.
, 2005, “
Evolution Strategy for Gasturbine Fault-Diagnoses
,”
Appl. Energy
0306-2619,
81
, pp.
222
230
.
11.
Toffolo
,
A.
, and
Lazzaretto
,
A.
, 2007, “
A New Thermoeconomic Method for the Location of Causes of Malfunctions in Energy Systems
,”
ASME J. Energy Resour. Technol.
0195-0738,
129
, pp.
1
9
.
12.
Lazzaretto
,
A.
, and
Toffolo
,
A.
, 2008, “
Prediction of Performance and Emissions of a Two-Shaft Gas Turbine From Experimental Data
,”
Appl. Therm. Eng.
1359-4311,
28
, pp.
2405
2415
.
13.
Diakunchak
,
I. S.
, 1992, “
Performance Deterioration in Industrial Gas Turbines
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
114
, pp.
161
168
.
14.
Toffolo
,
A.
, and
Benini
,
E.
, 2000. “
A New Pareto-Like Evaluation Method for Finding Multiple Global Optima in Evolutionary Algorithms
,”
Late Breaking Papers at the 2000 Genetic and Evolutionary Computation Conference
, Las Vegas, NV, Jul. 8–12, pp. 405–410.
You do not currently have access to this content.