Recent developments in monitoring of engines using acoustic emission

The application of acoustic emission (AE) for monitoring internal combustion (IC) engines is reviewed in this paper. Recent developments in monitoring mechanical events and processes using AE are discussed. The high spatial and temporal fidelity of the AE signals acquired from engines in service make it possible to focus monitoring strategies on individual events and processes. This gives AE the advantages of earlier fault diagnosis and source location whereas other techniques generally monitor symptoms of faults. Monitoring of engine speed, event timing, and reconstitution of processes such as injection and combustion are also possible. These capabilities of AE monitoring are discussed with reference to the development of AE data handling and analysis approaches for engines.

[1]  J A Twiddle,et al.  A high-level technique for diesel engine combustion system condition monitoring and fault diagnosis , 2002 .

[2]  Robert Lewis Reuben,et al.  The role of acoustic emission in industrial condition monitoring. , 1998 .

[3]  John Alexander Steel,et al.  Detection of diesel engine faults using acoustic emission. , 1998 .

[4]  Xiaoli Li,et al.  On-line tool condition monitoring system with wavelet fuzzy neural network , 1997, J. Intell. Manuf..

[5]  Robert Lewis Reuben,et al.  Acoustic emission source discrimination using a piezopolymer based sensor , 1999 .

[6]  Fengshou Gu,et al.  Acoustic based condition monitoring of a diesel engine using self-organising map networks , 2002 .

[7]  S J Wilcox,et al.  Acoustic emission monitoring of tool wear during the face milling of steels and aluminium alloys using a fibre optic sensor. Part 2: Frequency analysis , 1997 .

[8]  L. M. Rogers,et al.  Use of acoustic emission methods for crack growth detection in offshore and other structures. Discussion , 1998 .

[9]  John Alexander Steel,et al.  THE DEVELOPMENT OF AUTOMATED PATTERN RECOGNITION AND STATISTICAL FEATURE ISOLATION TECHNIQUES FOR THE DIAGNOSIS OF RECIPROCATING MACHINERY FAULTS USING ACOUSTIC EMISSION , 2003 .

[10]  Kevin M. Buckley,et al.  Fault monitoring using acoustic emissions , 1999, Smart Structures.

[11]  John Alexander Steel,et al.  Exhaust Valve Leakage Detection in Large Marine Diesel Engines , 1998 .

[12]  A. J. Morris,et al.  Wavelets and non-linear principal components analysis for process monitoring , 1999 .

[13]  S. H. Carpenter,et al.  Correlation of the acoustic emission and the fracture toughness of ductile nodular cast iron , 1991, Journal of Materials Science.

[14]  Juha Miettinen,et al.  Acoustic emission in monitoring sliding contact behaviour , 1995 .

[15]  David Dornfeld,et al.  Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring , 1990 .

[16]  Karen Margaret Holford,et al.  Acoustic Emission Source Location , 1999 .

[17]  John Alexander Steel,et al.  A study of the variability of acoustic emission signals from a medium size marine diesel engine under service conditions , 2003 .

[18]  John Alexander Steel,et al.  Detection of incipient cavitation in pumps using acoustic emission , 1997 .

[19]  L. K. Hansen,et al.  On condition monitoring of exhaust valves in marine diesel engines , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[20]  Noel E. Sharkey,et al.  Acoustic emission, cylinder pressure and vibration: a multisensor approach to robust fault diagnosis , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[21]  J A Twiddle,et al.  Fuzzy model-based condition monitoring and fault diagnosis of a diesel engine cooling system , 2002 .

[22]  Duncan P. Hand,et al.  Acoustic emission monitoring of tool wear during the face milling of steels and aluminium alloys using a fibre optic sensor. Part 1: Energy analysis , 1997 .

[23]  J. Yang,et al.  Diagnosis of moving components of internal combustion engines by analysing vibration signals , 1996 .

[24]  S. Keyvan,et al.  Feature extraction of metal impact acoustic signals for pattern classification by neural networks , 1997 .

[25]  Robert Lewis Reuben,et al.  AE mapping of engines for spatially located time series , 2005 .