About this event

Dr Gbanaibolou Jombo CEng MIMechE, FHEA, PhD, MSc, PGCert, BTech

Senior Lecturer in Machine Performance and Structural Integrity

Department of Engineering,

School of Physics, Engineering and Computer Science,

University of Hertfordshire,

College Lane Campus, Hatfield, AL10 9AB, United Kingdom.

Dr Gbanaibolou Jombo is a Senior Lecturer in Machine Performance and Structural Integrity at the School of Physics, Engineering, and Computer Science, University of Hertfordshire, UK. His research focuses on diagnostics, prognostics, and reliability of engineering systems. He has been involved in consultancy with several companies and provides expertise in noise and vibration, machine condition monitoring, and structural health monitoring. Previously, he was a KTP Research Associate in the School of Engineering, University of Lincoln working on a joint project with Siemens Industrial Turbomachinery Lincoln on the development of intelligent systems for the testing and monitoring of industrial gas turbines. He completed his PhD from Cranfield University, working on the integrated application of vibration and gas path analysis for the monitoring of fouling in a gas turbine compressor. He received the John Greenlees Findlay Memorial Award in Recognition of Endeavour & Initiative for his MSc in Design of Rotating Machines in Cranfield University.

ABSTRACT

Acoustic-Based Machine Condition Monitoring: Methods and Challenges

The traditional means of monitoring the health of industrial systems involves the use of vibration and performance monitoring techniques amongst others. In these approaches, contact-type sensors, such as accelerometer, proximity probe, pressure transducer and temperature transducer, are installed on the machine to monitor its operational health parameters. However, these methods fall short when additional sensors cannot be installed on the machine due to cost, space constraint or sensor reliability concerns. On the other hand, the use of acoustic-based monitoring technique provides an improved alternative, as acoustic sensors (e.g., microphones) can be implemented quickly and cheaply in various scenarios and do not require physical contact with the machine. The collected acoustic signals contain relevant operating health information about the machine; yet they can be sensitive to background noise and changes in machine operating condition. These challenges are being addressed from the industrial applicability perspective for acoustic-based machine condition monitoring. This talk presents the development in methodology for acoustic-based fault diagnostic techniques and highlights the challenges encountered when analysing sound for machine condition monitoring.