A systematic data science approach towards predictive maintenance application in manufacturing industry
Typ
Examensarbete för masterexamen
Program
Product development (MPPDE), MSc
Publicerad
2021
Författare
Vasudevan, Adarsh
Duan, Xinjie
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The use of data and obtaining insights from the data through data science approaches
is invading every domain of industrial applications. Today, most of the
industries harness the power of data to assess their performance and also to find
any possible chance for improvement. This thesis project explores the data driven
approach in order to perform a diagnostics and predictive analysis on the industrial
alarm data collected from Volvo Group, using a systematic data scientific methodology.
The company is currently facing with an issue of having machine stoppages due to
the triggering of alarms, for which the cause is unknown. The machine performs
a process of Part A1 and Part A2 used in the engines of the trucks. Therefore,
the company is having challenge in finding the root cause of the alarms and also
to get insights into correlation of alarms and the product types being processed in
the machines, which is Part A1 and Part A2. Therefore, this thesis aims to find the
combination of Part A1 arm and Part A2, which produces or triggers more alarms in
the specified machine chosen for the study. CRISP-DM methodology is been followed
to perform the data driven approach and thereby to answer the research questions
put forward in the earlier stages of the thesis. The data is obtained from 2 sources,
namely Manufacturing execution system and process data which is then integrated
to perform the further analysis in which exploratory data analysis is performed and
the list of Part A1 - Part A2 combination which triggers more alarms are been
found out. Along with this, the behaviour of problematic alarms are analysed and
more insights are obtained and reported. After the exploratory data analysis, the
predictive analysis is performed using machine learning models in which the multi
class classification models are generated using different machine learning models
wherein the model predicted the alarm category based on the selected input variables
used in the modelling. The decision tree model is selected based on the accuracy
score and its corresponding rules are derived and the rule with higher accuracy is
chosen to provide as a decision support to the company. As a practical contribution
of this thesis, it is proposed that more historical data can be used for the analysis
and also the data from the quality department can be used to explore the possible
in-depth root causes. Finally, this thesis introduced the novelty of implementing
predictive modelling in industrial alarm problems and hence produced an academic
contribution.
Beskrivning
Ämne/nyckelord
Exploratory data analysis (EDA), Machine learning, Multi-class classification , Maintenance, Alarm management