Early detection of contact distress for enhanced performance monitoring and predictive inspection of machines

Lead Research Organisation: University of Southampton
Department Name: Faculty of Engineering & the Environment


Monitoring the health of tribocontacts requires the study of friction, tribofilm integrity, and wear transitions. These challenge experimental tribologists to develop accurate methods for in-situ measurements and ideally continuous monitoring. Indirect measurements such as friction changes, sudden heating, changes in vibration or debris in the oil can detect severe wear transitions but cannot detect the subtle mechanistic changes which occur in unhealthy evolution of the contact. However, surface charge generated by tribocontacts and measured by single macro sensors, has detected tribological features such as tribofilm chemistry, adhesive wear, abrasive wear, phase transformations and wear debris but over large surfaces areas. This proposal, therefore, will miniaturise existing sensing technology, with embedded electronics to overcome signal to noise issues, and use arrayed sensors for augmented sensing, and machine learning. The sensor array /learning system would be trained to detect early evidence of lubricated contact decay from charge maps of the surface and allow better prediction of remaining useful life or, what corrective adjustment is needed in running conditions, to assure operational integrity.

Planned Impact

Our plan to deliver industrial, academic and societal impact includes the following actions.

ECONOMIC IMPACT: We will pro-actively grow our pool of partners through the course of the grant by engaging with relevant networks within Knowledge Transfer Network Ltd, the UK Tribology (including IMechE, IoP, IoM3, IET and RSC) and this project will benefit from a unique University of Southampton initiative called Future Worlds (futureworlds.com) which is a vibrant network for connecting academic researchers with external collaborators and investors. A Stakeholder Steering group will be formed of industrial partners will help with effective dissemination of the latest findings to key industrial sectors. Workshops will be held at Southampton and synergy sought between the two RAEng Visiting industrial chairs to nCATS (Prof Honor Powrie from GE and Prof Walter Holweger from Schaeffler.

SOCIETY IMPACTS: Tribology spans fundamental science through engineering to real-world applications and this project provides an exciting opportunity to showcase the value of such research and to engage the next generation. The material understanding will be promoted via website and promotion brochures etc. Showcase events will be arranged to link to a wide range of conferences covering machine learning, condition monitoring, sensors and tribology. Outcomes will be used at Bringing Research to Life roadshow (reaching some 20,000 people annually) and the Winchester Science Centre. We will also work with the Public Policy@Southampton team to help researchers reach and influence key policy makers to press home the message that understanding tribology enables companies to save of energy costs and reduce emissions as well as enable new product designs based on smart machines. In addition during National Science and Engineering Week public lectures will be given.

KNOWLEDGE IMPACT: We will publish our work in the highest quality academic journals as well as present our work at seminars and at the major international conferences. Our contacts will act as advocates for the research in their various communities. The project would also inform the ISO standard ISO 13374 on Machinery CM & Diagnostics.

PEOPLE IMPACT: Academic and research staff will learn from the multidisciplinary collaborations and joint working embedded within each work package design. Researchers will be offered specific placement opportunities with the industrial collaborators. PDRAs and PhDs will join the vibrant research community at Southampton which includes targeted ECR events.


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Description Heath monitoring of bearings 
Organisation Schaeffler
Department Schaeffler Technologies AG & Co. KG – Herzogenaurach
Country Germany 
Sector Private 
PI Contribution supervising two industry funded PhDs
Collaborator Contribution access to expertise, special testing facilities and coating, sensor technologies
Impact PhD supervision
Start Year 2019
Description Machine learning 
Country United Kingdom 
Sector Private 
PI Contribution running different machine learning algorithms on their test sets
Collaborator Contribution real engineering sensors data to test ML codes
Impact real engineering sensor data supplied to test our machine learning toolbox
Start Year 2020
Description early wear monitoring 
Organisation Afton Chemical
Country United States 
Sector Private 
PI Contribution we are monitoring early wear in lubrication rigs
Collaborator Contribution access to expertise, wear rigs and industry standard gear testing rigs
Impact chemistry and mechanical engineering and electronic engineering
Start Year 2022
Title Python script for reading '.hpf' files 
Description Python script for reading '.hpf' files 
Type Of Technology Webtool/Application 
Year Produced 2020 
Impact eads QuickDAQ hpf files, returns 'filename_info.txt', 'filename_data.csv' files file contains function 'write_info_and_csv_from_hpf' which takes a filename as input, and has optional output filename argument. This reads through the '.hpf' file and returns an info file as txt, has the name 'filename_info.txt' and the data in double format as a csv file, called 'filename_data.csv' which has the data as a single column, and the header is the channel number. Unfortunately at present I only have single channel hpf data files, so I've not quite finished the coding for outputting as multichannel data. I will update if I get a multichannel '.hpf' file. This was originally written by translating the matlab code from https://www.datatranslation.de/Download/Demos/Hpf2Matlab_Rev2_0.m 
URL https://eur03.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fjogrundy%2FRead_Quick...
Description Artificial Intelligence for Scientific Discovery (AI3SD) Winter Seminar Series 2020/21 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact Artificial Intelligence for Scientific Discovery (AI3SD) Winter Seminar Series 2020/21 'Outlier Detection in Scientific Data' Jan 20th 2021, Seminar
Centre For Machine Intelligence Show Case event, 2019, 'Outlier Detection on Sensor Data' Poster
Year(s) Of Engagement Activity 2020,2021
Description Austria conference: ÖTG-SYMPOSIUM 2019 Title: Monitoring the health of tribo-contacts 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Industry/Business
Results and Impact OTG-Symposium is an opportunity to gain insights relating to tribology which is the science and technology of interacting surfaces in relative motion. The event explores the application of knowledge from all areas of tribological research to control friction and wear, in particular through the optimal design of functional elements.
Year(s) Of Engagement Activity 2019
Description Sensors and the IoT for the Environment at Symposium on High-Tech Collaborative Innovation, Quingdao City, China 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Other audiences
Results and Impact Invited Talk: Sensors and the IoT for the Environment at Symposium on High-Tech Collaborative Innovation, Quingdao City, China
Year(s) Of Engagement Activity 2020