Condition Monitoring Algorithms in MATLAB® (Springer Tracts in Mechanical Engineering)

✍ Scribed by Adam Jablonski

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 542
• Category: Library

✦ Synopsis

This book offers the first comprehensive and practice-oriented guide to condition monitoring algorithms in MATLAB®. After a concise introduction to vibration theory and signal processing techniques, the attention is moved to the algorithms. Each signal processing algorithm is presented in depth, from the theory to the application, and including extensive explanations on how to use the corresponding toolbox in MATLAB®. In turn, the book introduces various techniques for synthetic signals generation, as well as vibration-based analysis techniques for large data sets. A practical guide on how to directly access data from industrial condition monitoring systems (CMS) using MATLAB® .NET Libraries is also included. Bridging between research and practice, this book offers an extensive guide on condition monitoring algorithms to both scholars and professionals.

“Condition Monitoring Algorithms in MATLAB® is a great resource for anyone in the field of condition monitoring. It is a unique as it presents the theory, and a number of examples in Matlab®, which greatly improve the learning experience. It offers numerous examples of coding styles in Matlab, thus supporting graduate students and professionals writing their own codes."

Dr. Eric Bechhoefer

Founder and CEO of GPMS

Developer of the Foresight MX Health and Usage Monitoring System

✦ Table of Contents

Foreword
Preface
Acknowledgements
Contents
About the Author
Abbreviations
1 Introduction
Abstract
1.1 Why Machinery is Measured?
1.2 What is Signal Processing Method Development?
1.3 Commercialization of Novel Diagnostic Techniques
1.3.1 Case-Based Deduction
1.3.2 Supervised Data Acquisition
1.3.3 Multidimensional Analysis
1.4 Supporting CMS with MATLAB®
1.5 The Scope
References
2 Principles of Condition Monitoring Systems
Abstract
2.1 Overview
2.2 CMS Tasks
2.3 CMS Classification
2.3.1 Tasks-Based Classification
2.3.2 Discussion on CMS-Related Glossary
2.3.3 Other Classifications
2.4 Stationary CMS Architecture
2.5 CMS Configuration and Implementation
2.5.1 Implementation Stages
2.5.2 CMS Selection
2.5.3 Initial Configuration, Installation, and Commissioning
2.5.4 Post-Reference Configuration Tuning
2.5.5 Final System Tuning
2.6 CMS Operation and Support
References
3 Vibration Components Generated by Rotary Machinery
Abstract
3.1 Hardware Parameters
3.2 Vibration Signature
3.3 The Concept of “Characteristic Frequency”
3.4 The Concept of “Characteristic Order”
3.5 Basic Mechanical Components
3.5.1 Shafts
3.5.2 Blades
3.5.2.1 Common-Speed Machinery
3.5.2.2 Fast-Speed Machinery
3.5.3 Gearboxes
3.5.3.1 Parallel Gearboxes
3.5.3.2 Epicyclic Gearboxes and Multi-stage Gearboxes
3.5.4 Rolling Element Bearings (REBs)
3.6 Harmonic Components
3.6.1 Why Harmonic Components Are Generated in Spectrum?
3.6.2 Why Harmonic Components Are Generated in Envelope Spectrum?
3.6.3 What Are Harmonics of Harmonics?
3.7 Note on Additional Modulations
References
4 Signal Processing Algorithms
Abstract
4.1 Basic Preprocessing Algorithms
4.1.1 Scaling
4.1.2 Windowing and Compensation
4.1.3 Trimming and Zero-Padding
4.1.3.1 Overview
4.1.3.2 Numerical Example of One-Sided Preprocessing
4.1.3.3 Influence on a Spectrum
4.2 Basic Processing Algorithms
4.2.1 Filtering
4.2.1.1 Overview
4.2.1.2 Filtering Paths and Filter Data Structures
4.2.1.3 Quick Command Line Filtering
4.2.1.4 Advanced Command Line Filtering
4.2.1.4.1 Quick Functions (digitalFilter Class)
Designfilt Function (digitalFilter Class)
Fdesign and Design Functions (DSP Toolbox™)
Butter Function (Filter Coefficients)
4.2.1.5 GUI-Supported Filtering
Filter Design
Filter Implementation of GUI-Generated Filters
Filter Analysis
4.2.1.6 User Frequency Domain Filtering
Synthetic Signal Description
Low-Pass Filtering
High-Pass Filtering
Band-Stop Filtering
Band-Pass Filtering
4.2.1.7 Function Reference
4.2.1.8 Beyond MATLAB®—Filter Coefficients in C Language
4.2.2 Resampling and Order Spectrum Basics
4.2.2.1 General Concept
4.2.2.2 Dictionary
4.2.2.3 List of Operations
4.2.2.4 Simplified Algorithm
4.2.2.5 Advanced Algorithm
4.2.2.6 Example 1—Synthetic Data
4.2.2.7 Example 2—Real Data
4.2.3 Integration
4.2.3.1 Noise-Free Synthetic Signal
4.2.3.2 Noisy Synthetic Signal
4.2.3.3 Real Signal
4.3 Combined Processing Algorithms
4.3.1 Signal Envelope and Envelope Spectrum
4.3.1.1 Overview
4.3.1.2 MATLAB® Documentation and Functions
4.3.1.3 Step-by-Step Classical Time-Domain Signal Envelope Example
4.3.1.4 Analytical Considerations
4.3.1.5 General Envelope Calculation Function
4.3.1.6 PATH 1: Envelope Spectrum from Time-Domain Filtered Signal
4.3.1.7 PATH 2: Envelope Spectrum from Frequency-Domain Filtered Signal
4.3.1.8 PATH 3: Envelope Order Spectrum from Angle-Domain Filtered Signal
4.3.1.9 PATH 4: Envelope Order Spectrum from Order-Domain Filtered Signal
4.3.1.10 Summary
4.3.2 Velocity and Displacement Spectrum from Acceleration Signal
4.3.2.1 Overview
4.3.2.2 Algorithms in a Nutshell
4.3.2.3 Signal Processing Blocks
4.3.2.3.1 HP Filtration
4.3.2.3.2 Integration
4.3.2.3.3 Signal Scaling
4.3.2.3.4 Resampling
4.3.2.3.5 Calculation of Spectral Amplitudes
4.3.2.3.6 Calculation of Spectral Resolution
4.3.2.4 Examples
4.3.2.4.1 List of Signals
4.3.2.4.1 Single Sinusoidal Component
4.3.2.4.3 Variable Speed Component
4.3.2.4.4 Real Rotary Machinery Signal
4.3.2.5 Influence of Filtering Domain
4.3.2.6 Omega Arithmetics
4.3.2.7 Displacements Spectrum
4.3.2.7.1 General Concept
4.3.2.7.2 Single Sinusoidal Component
4.3.2.7.3 Real Rotary Machinery Signal
4.3.3 Time Synchronous Averaging (TSA)
4.3.3.1 Overview
4.3.3.2 Time-Domain and Angle-Domain Waveforms
MATLAB® “tsa” Function
4.3.3.2.2 User Analysis of Rational Transmission Ratios
4.3.3.3 Resampled Signals Spectral Analysis
4.3.3.4 TSA Signals Spectral Analysis
4.4 Instantaneous Speed Calculation and Reconstruction
4.4.1 Basic Operations on Real Phase Marker (PM) Signal
4.4.2 Tachorpm Function
4.4.3 Explanation of Instantaneous Speed Shape Profile
4.4.4 Extraction of Instantaneous Speed from Tacholess Vibration Signal
4.4.4.1 Overview
4.4.4.2 Simple Sinusoidal Signals
4.4.4.2.1 Findpeaks Function
4.4.4.2.2 User Pulse Detection Function
4.4.4.2.3 Signal Phase Demodulation
4.4.4.3 Complex Real Signals
4.4.4.3.1 General Concept
4.4.4.3.2 Limited Speed Fluctuation (Direct Phase Demodulation)
4.4.4.3.3 Urbanek’s Two-Step Procedure in RPM Track Tool
4.4.4.3.4 Polygon-Based Reconstruction
4.4.5 Summarizing Comparison
4.5 Other SP Algorithms
References
5 Vibration-Based Condition Assessment Methods
Abstract
5.1 Overview
5.2 MATLAB® Predictive Maintenance Toolbox™
5.3 Protection Scalar Health Indicators
5.3.1 Acceleration PP
5.3.2 Acceleration RMS
5.3.3 Velocity RMS
5.3.3.1 Overview
5.3.3.2 VRMS Calculated in Time-Domain
5.3.3.3 VRMS Calculated in Frequency-Domain
5.3.3.4 Practical Considerations
5.4 Monitoring Scalar Health Indicators as Trend Data
5.4.1 Classification of Monitoring Health Indicators
5.4.2 Trend Data
5.5 Evaluation of Scalar Health Indicators
5.5.1 General Evaluation Paths
5.5.2 Extraction of Narrowband Signals
5.5.3 Evaluation of Spectral Amplitudes of Narrowband Signals
5.5.4 Evaluation of Spectral Indexes of Narrowband Health Indicators
5.6 Extended Role of Scalar Indicators in Diagnostic System
5.7 Diagnostic Figures
5.7.1 Classification and Selection
5.7.2 2-D Figures Generated from a Single Continuous Signal
5.7.2.1 General Concept
5.7.2.2 Time-Domain Waveform (TD)
5.7.2.3 Spectrum (S)
5.7.2.4 Averaged Spectrum (AS)
5.7.3 3-D Figures Generated from a Single Signal (Colormaps)
5.7.4 3-D Figures Generated from Array of Discontinuous Signals
5.8 A Note on Selected Time–Frequency Representations
5.8.1 General Relations
5.8.2 Streaming Data Relations
5.8.2.1 Time-Domain Relations
5.8.2.2 Frequency-Domain Relations
5.8.3 Diagnostic Relations
References
6 Synthetic Signals Generation Methods
Abstract
6.1 Overview
6.2 Sinusoidal Components
6.2.1 Sinusoidal Template
6.2.2 Basic Operations on a Single Sinusoidal Component
6.2.2.1 Amplitude Modulation (AM)
6.2.2.1.1 Overview
6.2.2.1.2 Periodic Amplitude Modulation (P-AM)
6.2.2.1.3 Non-Periodic Monotonic Amplitude Modulation (NPM-AM)
6.2.2.1.4 Non-Periodic, Quasi-Monotonic Amplitude Modulation (NPQM-AM)
6.2.2.1.5 Non-Periodic Generalized Amplitude Modulation (NPG-AM)
6.2.2.2 Frequency Modulation (FM)
6.2.2.2.1 Overview
6.2.2.2.2 Periodic Frequency Modulation (P-FM)
6.2.2.2.3 Non-Periodic Monotonic Frequency Modulation (NPM-FM)
6.2.2.2.4 Non-Periodic Generalized Frequency Modulation (NPG-FM)
6.2.3 Combinations of Basic Operations
6.2.3.1 Oscillatory Signal (P-AM with P-FM)
6.2.3.2 Run-Up with Critical Speed (NPM-AM + CS Window with NPM-FM)
6.2.3.3 Generation of Pseudo-FRF (PFRF) Profile
6.2.3.4 Generalized AM and FM Modulation
6.3 Decaying Pulses
6.3.1 Basic Operations
6.3.1.1 Determination of Time of Pulses
6.3.1.2 Simplified Signal with Deterministic Carrier
6.3.1.3 Pulses with Narrowband Random Carrier Signal
6.3.1.4 Pulses with Periodically Oscillating Repetition Rate
6.3.1.5 Pulses with Monotonically Increasing Repetition Rate
6.3.1.6 Pulses with Generalized Frequency-Modulated Repetition Rate
6.3.1.7 Additional Phase-Locked AM
6.3.2 Combinations of Operations
6.3.2.1 Oscillatory Pulses
6.3.2.2 Run-Up with Critical Speed
6.3.2.3 Additional Phase-Locked Non-Periodic AM
6.3.2.4 Generalized AM and FM Modulation
6.3.3 Jitter
6.3.3.1 Overview
6.3.3.2 Jitter as a Cumulative Variable
6.3.3.3 Jitter as Independent Variable
6.3.3.4 Combination of Operations
References
7 Simulating Operational Signals
Abstract
7.1 Concept Description
7.2 Simulated Object Description
7.2.1 Mechanical Parameters
7.2.2 Characteristic Orders and Frequencies
7.3 Signal Generation Graphical Framework
7.3.1 Slow Shaft Imbalance
7.3.2 REB Local Inner Race Fault
7.4 Code Description
7.4.1 Data Acquisition Parameters
7.4.2 Generation of User Profiles
7.4.2.1 Overview
7.4.2.2 Profile #1: Operational Speed Profile
7.4.2.3 Profile #2: Pseudo-FRF Profile
7.4.2.4 Profile #3: Structural Noise Profile
7.4.3 Definition of Principal Static Data
7.5 Signal Components
7.5.1 Fast Shaft and Slow Shaft
7.5.2 Gearbox
7.5.3 Rolling Element Bearing
7.5.4 Random Noise
7.6 Finalization
7.7 Resultant Signal
7.7.1 Two-Dimensional Plots
7.7.2 Selected Spectrogram Visualization
References
8 Simulating Long-Term Machine Fault Development
Abstract
8.1 Overview
8.2 Failure Mode and Failure Development Function (FDF)
8.3 General Scheme of Long-Term Data Generation
8.4 Relations Between Profiles
8.5 Code Description
8.5.1 STAGE No. 1: Generate Failure Mode Functions
8.5.2 STAGE No. 2: Define Data Acquisition Parameters
8.5.3 STAGE No. 3: Generate Common-Pseudo PFRF
8.5.4 STAGE No. 4: Generate (and Store) Common Structural Noise Base
8.5.5 STAGE No. 5: Generate and Save Signals
8.5.5.1 General Information
8.5.5.2 Function Description
8.5.5.3 Making Data Folders
8.5.5.4 Creating General Variables
8.5.5.5 Main Loop
8.5.5.6 Finalization
8.6 Generated Data
8.7 A Note on the Role of Random Numbers
9 Analysis of Long-Term Fault Development
Abstract
9.1 How Large Industrial Data Sets Are Analyzed?
9.2 Statistical Data Analysis
9.3 Narrowband Spectral Analysis
9.3.1 General Information
9.3.2 Selected Results
9.4 Two-Dimensional (Spectral) Comparison
9.4.1 General Information
9.4.2 Selected Results
9.4.2.1 No Fault Case
9.4.2.2 Imbalance Detection and Identification
9.4.2.3 Gearbox Fault Detection and Identification
9.4.2.4 REB Fault Detection and Identification
9.4.2.5 Combined Faults
9.5 Three-Dimensional Visualization
9.5.1 General Information
9.5.2 Selected Results
9.5.2.1 Imbalance Detection and Identification
9.5.2.2 Gearbox Fault Detection and Identification
9.5.2.3 REB Fault Detection and Identification
9.5.2.4 Multi-fault Detection
9.5.3 MATLAB® Code for 3-D Analysis of Multiple Discontinuous Signals
9.5.3.1 Code Framework
9.5.3.2 Step_1_Select_Data_Folder
9.5.3.3 Step_2_Select_Spectrum_Type_and_Parameters
9.5.3.4 STEP_3_CONSTRUCT_ARRAY—Algorithm
9.5.3.5 STEP_3_CONSTRUCT_ARRAY—Code
9.5.3.6 Step_4_Generate_Abscissa
9.5.3.7 Step_5_Select_Display_Options
9.5.3.8 Step_6_Plot
9.5.3.9 A Note on Selection of Plotting Function
9.6 Comparison of Methods
Reference
10 Connecting MATLAB® to CMS
Abstract
10.1 The Role of a PC Computer
10.2 Data Flow Directions
10.3 Data Type-Based Roles of MATLAB®
10.4 Data Type Conversion
10.5 Array Data and Metadata
10.6 Exporting Data from MATLAB®
10.6.1 Overview of Methods
10.6.2 Manual Data Export
10.6.3 Programmable Data Conversion
10.6.3.1 Before Exporting the Data
10.6.3.2 Exporting via fprintf Function
10.6.3.3 Exporting via Other Functions
10.6.3.4 Summary
10.6.4 Numerical Data Precision
10.6.4.1 Basic Concept
10.6.4.2 Full Precision
10.6.4.3 Data Compression and Placement of the Phase Marker (PM) Bit
10.6.4.4 Loop Design
10.7 Importing Data into MATLAB®
10.7.1 Overview of Methods
10.7.2 Interactive Data Import
10.7.3 Programmable Data Import
10.7.4 Direct Access to CMS Replicated Data Using CMS Libraries
References
11 Development of Interface for Direct Data Access (DDA)
Abstract
11.1 Basic Concept
11.2 Step-by-Step Procedure
11.3 CMS Configuration Handling
11.4 Additional Concerns
11.4.1 File Versus Database Data Source
11.4.2 Permission Levels
References
12 Prototype Tools
Abstract
12.1 Overview
12.2 Script-Based Testing of Health Indicators
12.2.1 MATLAB® Code for Direct Data Access
12.2.2 Shaft Imbalance Example
12.3 GUI-Based Spectral Analysis of Large Data Sets
12.3.1 MATLAB® Code for Direct Data Access
12.3.2 Shaft Imbalance Example
12.3.3 Pump Cavitation
12.4 Other Concepts
12.4.1 CMS Performance Display
12.4.2 Threshold Configuration
12.5 Summary of Prototype Tools
Index