Centre for Efficiency and Performance Engineering

  • United Kingdom

Organization profile

Profile Information

The Centre for Efficiency and Performance Engineering (CEPE) aims to scope and sensitivity of machinery fault detection and diagnosis and of plant performance and emissions monitoring. Headed by Professor Andrew Ball, the CEPE at the University of Huddersfield is the largest independent group of its type in the world. Its mission is to tackle real-world problems using the most practical and accessible means possible. 

The Centre specialises in vibro-acoustics, vibro impacts, instantaneous angular speed and instantaneous electric current analysis, dagnostic model development, signal processing, feature extraction, pattern recognition, sensor development, non-intrusive parameter estimation and model based fault diagnosis. Internationally recognised as one of the world's leading research organisations in the field of plant condition and performance monitoring, our researchers have been developing capabilities for various machines since 1991.

Fingerprint The fingerprint is based on mining the text of the scientific documents related to the associated persons. Based on that an index of weighted terms is created, which defines the key subjects of research unit

Bearings (structural) Engineering & Materials Science
Condition monitoring Engineering & Materials Science
Failure analysis Engineering & Materials Science
Fault detection Engineering & Materials Science
Signal analysis Engineering & Materials Science
Reciprocating compressors Engineering & Materials Science
Modulation Engineering & Materials Science
Engines Engineering & Materials Science

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Research Output 2008 2019

Open Access
Naive Bayes Classifier

Extraction of the largest amplitude impact transients for diagnosing rolling element defects in bearings

Hu, L., Zhang, L., Gu, F., Hu, N. & Ball, A., 1 Feb 2019, In : Mechanical Systems and Signal Processing. 116, p. 796-815 20 p.

Research output: Contribution to journalArticle

Bearings (structural)
White noise

A Gas Path Fault Contribution Matrix for Marine Gas Turbine Diagnosis Based on a Multiple Model Fault Detection and Isolation Approach

Yang, Q., Li, S., Cao, Y., Gu, F. & Smith, A., 1 Dec 2018, In : Energies. 11, 12, p. 1-21 21 p., 3316.

Research output: Contribution to journalArticle

Open Access
Fault Detection and Isolation
Gas Turbine
Multiple Models
Fault detection
Gas turbines

Projects 2015 2021