Big data for predictive maintenance

Lead Research Organisation: Cranfield University
Department Name: Sch of Aerospace, Transport & Manufact


Provide the state of art regarding tools and techniques used for big data and predictive maintenance.
Implement and validate models for feature extraction and fault detection and isolation or diagnostics.
Implement and validate models for fault prognostics.
Design, implement and validate the framework
Stage 1: Establish base lines and literature review. Examine common practices, and discoveries in the field related to techniques and process, and common tools and infrastructures used. Risk assessment.
Stage 2: Identify research gap, and review aims. Data collection, establish requirements for the framework requirement. Data cleaning and preparation, data management plan.
Stage 3: Using a condition based maintenance process, begin data analysis. Feature extraction; fault detection and isolation; diagnosis and prognostics using novel "big data" tools and knowledge discovery techniques.
Stage 4: Results visualisation; framework implemented and validated.
The work aims at a novel approach to time series analysis for system and machine faults, integrating physics, engineering know-how and discovered data patterns. The work has been trialled on train engine performance behaviour, door response, and wheel slip, seeking early and robust detection, diagnosis and prognosis.

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R512515/1 30/09/2017 29/09/2021
2203091 Studentship EP/R512515/1 20/09/2017 20/03/2021 Bernadin Namoano
Description -Models have been build for predictive maintenance with application to railways train equipment.
-Fault types have been found in the data using the designed models
Exploitation Route - Models built may be useful for detecting incipient fault or anomalies in streaming datasets. Therefore, it can be applied to many cross disciplinary dealing with anomalies, and surprising pattern detection
Sectors Aerospace, Defence and Marine,Digital/Communication/Information Technologies (including Software),Financial Services, and Management Consultancy,Healthcare,Transport,Other

Description The current findings are used to detect earlier degradation in trains assets such as passengers doors, train engines and wheel set to improve the passengers journey and reduce unplanned maintenance costs.
Sector Transport
Description EPSRC doctoral prize
Amount £1,584,860 (GBP)
Funding ID EP/T518104/1 
Organisation Cranfield University 
Sector Academic/University
Country United Kingdom
Start 03/2021 
End 03/2023
Title Doors datasets 
Description Data represents urban train condition monitoring raw data. Data contains, current and door motions during opening and closing periods. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
Impact Doors datasets present current and and speed profiles for train doors. Doors present normal and faulty conditions which can be served as basis for other researchers to device methods for fault detection. 
Title Engines datasets 
Description Data represents urban DMU trains engines condition monitoring data. 
Type Of Material Database/Collection of data 
Year Produced 2020 
Provided To Others? Yes  
Impact Data present normal and abnormal engine operations 
Description Partnership with Instrumentel 
Organisation Instrumentel
Country United Kingdom 
Sector Private 
PI Contribution Data provided by the company helped for the demonstration cases studies in the thesis.
Collaborator Contribution The company helps understanding data gathering context, signal description and several uncertainties.
Impact Outcome of the the collaboration includes: - Train door fault diagnosis - DMU engines fault diagnosis - Wheel slide recognition
Start Year 2017
Description IFAC Workshop on Advanced Maintenance Engineering, Services and Technology 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Professional Practitioners
Results and Impact More than 100 hundred postgraduate and professional practitioner attended to the AMEST workshop, which sparked questions and discussion afterwards, the workshop reported high interest in engineering application of machine learning techniques as well as data framework architectures and data management tools
Year(s) Of Engagement Activity 2020
Description UkRRIN conference 
Form Of Engagement Activity Participation in an activity, workshop or similar
Part Of Official Scheme? No
Geographic Reach National
Primary Audience Industry/Business
Results and Impact Poster presentation was shared with delegates.
Year(s) Of Engagement Activity 2019