Dr. Jinghua Zhang

Senior Lecturer in Fire Safety Engineering

School of Engineering

JB Firth, JBF003

+44 (0) 1772 89 5686


Subject Areas: Fire and Fire Safety, Engineering

Jing is research active within the area of fire and hazards science and is a member of the Centre for Fire and Hazards Science.

Full Profile

Jing is a cross-disciplinary researcher, active in the fields of safety engineering, intelligent monitoring systems, sensor, information and communication technologies. He teaches both undergraduate and postgraduate in FSE, supervises research students in master and doctoral levels.


  • Fellow of Higher Education Academy
  • AM of Chartered Management Institute


Zhang, Jinghua (2010) Catastrophic failure prognosis of oil-Immersed high voltage transformers. In: Sixth International Seminar on Fire and Explosion Hazards, 11th - 16th April 2010, Leeds, UK.

More publications


PGCert, Research Student Supervision, UCLan

PhD, Electrical Engineering & Electronics (UK ORS awarded), University of Liverpool

MSc, Intelligence Engineering (distinction), University of Liverpool

BEng, Telecom & Electronic Engineering (1st class), Shenzhen University


Postdoctoral researcher, Loughborough University

Safety research engineer, Bombardier Transportation

Postdoctoral researcher, University of Liverpool


Personal Awards

Sabbatical leave and Livesey research awards 2012/13, UCLan

Staff merit awards (Individual), Loughborough University

UK university ORS awards

Postgraduate entry awards, University of Liverpool

Undergraduate annual excellent students awards, Shenzhen University

Teaching Activities and Responsibilities

Module lead for:

  • Skills for Fire Studies
  • Information Technology
  • Fire Protection
  • Computational Engineering
  • Research Methods


Fire safety engineering

Sensor and communication technology

Data/signal processing and information retrieval

Intelligent monitoring systems and failure prognostics


iSpace-2, A smart decision support system for fire emergencies

iSpace-1, A new data interpretation method of condition monitoring for a sustainable energy infrastructure