Big data for predictive maintenance

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

Abstract

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.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/R512515/1 30/09/2017 29/09/2021
2625252 Studentship EP/R512515/1 20/09/2017 20/03/2021 Bernadin Namoano