Anomaly Detection in MATLAB

Throughout the life cycle of a physical system, the occurrence of failures or malfunctions poses a potential threat to its normal functioning. To safeguard against critical interruptions, it becomes imperative to implement an anomaly detection system within the facility. Termed as a fault diagnosis system, this mechanism is designed to identify potential malfunctions within the monitored system. The pursuit of fault detection stands as a pivotal and defining phase in maintenance interventions, demanding a systematic and deterministic approach to comprehensively analyze all conceivable causes that might have led to the malfunction.

In this chapter, we will learn the basic concepts of anomaly detection systems and how to implement an anomaly detection system in MATLAB.

We’re going to cover the following main topics:

• Introducing anomaly detection and fault diagnosis systems
• Using machine learning (ML) to identify anomalous functioning...

Technical requirements

In this chapter, we will introduce basic ML concepts. To understand these topics, a basic knowledge of algebra and mathematical modeling is needed. A working knowledge of the MATLAB environment is also required.

To work with the MATLAB code in this chapter, you need the following files (available on GitHub at https://github.com/PacktPublishing/MATLAB-for-Machine-Learning-second-edition):

• GearboxAccData.xlsx
• AnomalyDetectGearBox.m
• DroneFaultDiagnosis.xlsx
• UAVFaultDiagnosis.m

Introducing anomaly detection and fault diagnosis systems

Anomaly detection and fault diagnosis systems are crucial components of various industries, particularly in areas where safety, reliability, and efficiency are of utmost importance, such as manufacturing, healthcare, finance, and cybersecurity. These systems aim to identify unusual or unexpected patterns, behaviors, or conditions in data, processes, or systems that may indicate the presence of faults, defects, or anomalies.

Delving into the realm of anomaly detection, this section provides a comprehensive overview, unraveling the key principles and methodologies employed in identifying deviations from the norm within diverse systems and datasets.

Anomaly detection overview

Anomaly detection is a technique used in data analysis and ML to identify data points or patterns that deviate significantly from the expected or normal behavior within a dataset. Anomalies, also known as outliers, are data points that do not conform...

Using ML to identify anomalous functioning

A gearbox, also known as a gear mechanism or transmission, is a mechanical device designed to transmit mechanical power from one component to another while altering the speed, torque, and direction of rotation. Gearboxes consist of a set of gears with different sizes and configurations that mesh to provide specific mechanical advantages based on the desired output.

In an automotive context, a gearbox, often referred to as a transmission or gearshift, is a critical component that plays a central role in controlling the power output of the engine and the vehicle’s speed. Anomaly detection in a gearbox involves identifying irregularities or deviations from the normal behavior of the gearbox components and their associated systems. Detecting anomalies in a car’s gearbox is crucial for ensuring the vehicle’s safety, reliability, and overall performance.

To promptly identify anomalies in a gearbox, we can use sensors such...

Building a fault diagnosis system using MATLAB

Technological advancements have given rise to highly capable aircraft that can autonomously manage flight operations. These aircraft belong to the category known as UAVs, meaning they can fly without human pilots. UAVs offer numerous advantages, including significantly reduced operating costs compared to traditional piloted aircraft, the ability to operate in environments unsuitable for human presence, and the capacity for timely aerial surveillance, such as during natural disasters.

Initially employed primarily for military purposes, UAVs were used in dull missions involving monotonous and lengthy surveillance and reconnaissance, as well as in dirty missions that posed risks to human pilots’ safety. They also played a crucial role in dangerous missions where human lives were at risk. However, today, they are considered the future of modern aeronautics. The vast potential of UAV technology, its successes in military operations...

Understanding advanced regularization techniques

Advanced regularization techniques are methods used in ML and statistical modeling to prevent overfitting and improve the generalization performance of models. Overfitting occurs when a model fits the training data too closely, capturing noise and irrelevant patterns, which leads to poor performance on unseen data. Regularization techniques introduce constraints or penalties to the model’s parameters during training to encourage simpler, more generalized models.

Understanding dropout

Dropout is a regularization technique used in NNs, particularly deep NNs (DNNs), to prevent overfitting. Overfitting occurs when an NN learns to fit the training data too closely, capturing noise and memorizing specific examples rather than generalizing from the data. Dropout is a simple yet effective method for improving a model’s generalization performance.

During the training phase, at each forward and backward pass, dropout randomly...

Summary

In this chapter, we saw how to implement an automatic fault diagnosis system in MATLAB. We started by introducing the essential concepts of anomaly detection and fault diagnosis. Then, we saw how to implement a system for identifying anomalous operations in MATLAB. We used vibrational data from a gearbox to train a model based on logistic regression. Subsequently, we used the same data, but this time using a model based on Random Forest to improve the performance of the predictive model.

In the next section, we implemented a model for identifying a fault in UAV propellers based on acoustic emission. We used a classification model based on an SVM.

In the final section, we introduced the most popular methods for regularizing algorithms to improve model performance.

In conclusion, this book serves as a comprehensive guide and invaluable resource for both beginners and seasoned practitioners navigating the dynamic landscape of machine learning. The book not only equips...