Project spotlight

Akilu Yunusa-Kaltungo

Development of a condition monitoring system for Ashaka Cement PLC, a subsidiary of Lafarge Cement PLC

Alumni - Reliability Engineering and Asset Management MSc

Best overall performance in 2008-2009 MSc Maintenance Engineering and Asset Management. Akilu Yunusa-Kaltungo was awarded a certificate in recognition of his excellent performance on the MSc programme in 2008-2009.

Student: Akilu Yunusa-Kaltungo

Degree: Full time, Direct Taught MSc in Maintenance Engineering and Asset Management

Qualification: BEng, MSc

Country: Nigeria

Sponsor: Petroleum Technology Development Fund, Federal Government of Nigeria

Collaboration: Ashaka Cement PLC  

Current position: Staff Training and Development Engineer, Lafarge Cement PLC (Ashaka Plant), Nigeria.

Awards: Best overall performance in MSc maintenance engineering and asset management (2008-2009) EFNMS award for best master thesis in maintenance from the UK (2010)

Supervisor: Dr Jyoti Sinha

I did my BEng in Mechanical Engineering in University of Maiduguri (Nigeria). Upon the successful completion of my first degree, I starteded working as a Graduate Tutor in Automobile Engineering in one of the institutions of higher education in Nigeria. After that I moved to practicing mechanical maintenance in both the private and public sectors in Nigeria.

During the course of my career in the cement industry, I have had the opportunity to work as a memmeber of a coal project team, plant reliability champion, methods engineer and now the Staff Training and Development Engineer. Due to my exposure to the field of maintenance, in 2008 I decided to take up my Master's Degree in Maintenance Engineering and Asset Management at The University of Manchester, one of the world's most prestigious universities. To be honest, the experience was awesome and if I had the opportunity to repeat it, I wouldn't hesitate for one second. I had the opportunity to be taught by experts in their various fields, as well as sharing experience with colleagues from various industries across the globe.

Project abstract

The MSc project was based on the early detection of incipient equipment failures through the optimization of conventional condition monitoring techniques, i.e. vibration, thremography, lube and wear debris analysis. Four major types of equipment have been considered as critical, namely the

  • coal mill drive assemblies,
  • coal mills, the bag house fans,
  • booster fans, and
  • bag houses.

Although the coal workshop has just been commissioned, with no records of failure, the use of the concepts of Failure Modes and Effects Analysis (FMEA) was used to determine the critical equipments, with sole emphasis on the impacts of the equipments' failures. Also, the recorded failures for the plant's existing raw mill workshop, by the plant's Advanced Downtime Analysis Program (ADAP) were used to identify some of the most common failure modes for the selected critical equipments, due to the similarities in configuration of the two workshops. A detailed technical specification of the critical equipments and their approximated capital costs were also provided.

Based on an extensive literature review, a suitable condition monitoring system has been proposed, which used vibration analysis as its preliminary fault identification and diagnostic technique, while other condition monitoring techniques such as oil and wear debris analysis, thermography, performance monitoring. The use of human senses was applied as confirmatory and complementary techniques. The list of instrumentations and their approximate costs, details of the various sensors required, sensors mounting and orientations, data acquisition, display and signal processing have been explained in this project.

Based on the criticality of the coal workshop to the cement manufacturing process, the proposed condition monitoring systems have been integrated, so as to enhance the display of overall machines' health trends to the managers and operators, and to ease the process of incipient machine fault detection and diagnostics by the maintenance personnel and experts. The approximated cost of setting up the proposed condition monitoring system was also found to constitute only a minute fraction of the approximated costs of the critical equipments.


Fig 1 Mounting arrangements and colour coding of the different sensors   Fig 2 Fault detection and diagnosis flow chart for the proposed CM system   Fig 3 Fault tree of some likely causes of vibration in machines
fault detection   fault tree  
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