Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring

1. Introduction and background

1.1 Historical review

1.2 Machine fault characteristics and fault diagnosis

1.2.1 Typical modes of machine fault vibration signals

1.2.2 Bearing fault diagnosis

1.2.3 Gear fault diagnosis

1.3 Sparsity and Sparsity measures

1.4 Summaries References


2. Basic signal processing transforms and analysis

2.1 Introduction

2.2 Fourier transform

2.3 Time, frequency, and time-frequency analysis

2.4 Digital filtering

2.5 Hilbert transform and envelope analysis

2.6 Impulsiveness and cyclo-stationarity analysis

2.7 Summaries References


3. Newly advanced sparsity measures for fault signature quantification

3.1 Introduction

3.2 Classic sparsity measures and properties

3.3 Frameworks of sparsity measures

3.4 Newly designed sparsity measures and statistical indices

3.5 Cyclo-embedded sparsity measures

3.6 Other sparsity-informed measures

3.7 Difference between sparsity measures and complexity measures

3.8 Summaries References


4. Classic and advanced sparsity measures-based signal processing technologies

4.1 Introduction

4.2 Fast kurtogram and its variants

4.3 Blind deconvolution and its variants

4.4 Other signal decomposition/processing methods

4.4.1 Features of signal decomposition methods

4.4.2 EMD, LMD-based methods

4.4.3 WPT, EWT, VMD-based methods

4.4.4 Time-frequency based methods

4.4.5 Sparse decomposition and representation

4.5 Impulsive mode decomposition

4.6 Summaries References


5. Sparsity measures data-driven framework based signal processing technologies

5.1 Introduction

5.2 Sparsity measures data-driven framework

5.3 Optimized weights theory and its extensions

5.4 Optimized weights spectrum-based index

5.5 Difference mode decomposition

5.6 Others

5.7 Summaries


6. Outlook References