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Table of contents
Acknowledgments
List of Abbreviations
1 Introduction
1.1 Motivation
1.2 Background
1.3 Problems Definition
1.4 Proposed Solutions
2. Related Work
3 Industrial Maintenance and Machine Learning
3.1 Maintenance
3.1.1 Reactive Maintenance
3.1.2 Preventive Maintenance
3.1.3 Predictive Maintenance
3.2 Machine Learning (ML)
3.2.1 Types of Machine Learning
4 Dataset and Faults
4.1 Experimental Setup
4.2 Gearbox Dataset
4.3 Machinery Fault database
4.4 Rotatory machine states
4.4.1 Normal
4.4.2 Imbalance
4.4.3 Horizontal misalignment
4.4.4 Vertical misalignment
4.4.5 Underhang bearing fault
4.4.6 Overhang bearing fault
5 Methods
5.1 Raw data / Sensors reading
5.2 Preprocessing
5.2.1 Standard Deviation
5.3 Machine Learning Pipeline
5.3.1 Decision tree
5.3.2 Random Forest
5.3.3 Ada-boost (Adaptive Boosting)
5.4 Deep Neural Network (DNN) Pipeline
5.4.1 Activation Function
5.5 Performance Evaluation
5.5.1 Confusion matrix
5.5.2 Accuracy
5.5.3 Error Rate (ERR)
5.5.4 True Positive Rate (TPR)
5.5.5 False Positive Rate (FPR)
5.5.6 Precision
5.5.7 F1-Score
5.5.8 Mean Squared Error (MSE)
5.5.9 AUC Score
5.5.10 ROC Curve
6 Results
6.1 Gearbox Fault Prediction
6.1.1 Performance Evaluation on raw data
6.1.2 Performance Evaluation of normalized data
6.2 Machinery Fault Prediction
6.2.1 Performance evaluation of ML model in MFP dataset
6.2.2 Performance evaluation of DNN model in MFP dataset
7 Discussion
7.1 Gearbox Fault Prediction
7.2 Machinery Fault Prediction
8 Conclusions
