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How to Use Machine Learning for Anomaly Detection and Condition Monitoring

How to Use Machine Learning for Anomaly Detection and Condition Monitoring

When analyzing real-world data sets, a common desire to determine the data points that stand out from the rest arises. These data points that stand out are known as anomalies.

The process in which these outstanding data points are determined is called anomaly detection. These anomalies are ordinarily brought about by errors in data. In some cases, the anomalies are brought about by underlying processes that are previously not known.

Detecting outliers correctly is an important task. You can see some of the real-world usages of anomaly detection later. It is important to know when the login attempts are coming from an actual user, and when they are not. A very high amount of login attempts at a short time span can be considered as an anomaly and might be an indication of a serious cyberattack.

Anomalies are broadly categorized into:

Point anomalies: A point anomaly is a single instance of data that considerably deviates from the other data points
Contextual anomalies: contextual anomalies are common in the time-series data. These anomalies that are specific to the context. They are dependent on contextual information.
Collective anomalies: These are a collection of anomalies that are of related data instances in an entire dataset.

Conditional monitoring, on the other hand, is the process in which the status of the functionality of a machine is determined. A case in point is a machine that is either a rotating one (e.g. a steam turbine or a water pump) or a non-rotating one (e.g. a valve) does reach a point where it fails to function optimally. This is indicative of the need for some maintenance to the machine for its full functionality.

There are several ways in which conditional monitoring is carried out in a machine. The most common method entails observing every sensor function for the machine and then giving it values; a minimum possible and a maximum possible value. Whenever the current value of the reading is within this range, the machine is functioning optimally.

On the other hand, when the reading is out of this range, the machine is not functioning optimally, which calls for action.

This procedure involves the use of hard-coded alarm limits – the minimum and maximum values. It is not an effective method for it is known to send many false alarms that are not quite indicative of the status of the functionality of a machine. Sometimes some alarms miss to go off leading to a decrease in the optimal functionality of the machine.

This leads to the decreased availability of the machine and a lot of time is used to manually detect the anomalies. Missed alarms are on the other hand costly for they can lead to damage to a certain function of a machine or even worse, the entire machine.

The above-named problems emanate from the same cause. That the status of a critical function of a machine cannot be reliably determined by the analysis of value on its own. To achieve the desired results of a proper diagnosis, a combination of various values ought to be done.

The various machine learning approaches used for anomaly detection are as discussed below;

1. Density-Based Anomaly Detection Method

Anomaly detection, also known as outlier detection is the process used to find data objects that possess behaviors that are different from the expectation.

The density-based approach for anomaly detection is based on the algorithm known as k-nearest neighbors. In a machine operating environment, the normal data points usually occur together in a dense neighborhood while those that have anomalies tend to be far from the normal ones.

Measuring the nearest set of a data point depends on the data type you have, after which the Euclidian distance or similar measure methods are used. The type of data could either be categorical or numerical.

2. Support Vector Anomaly Detection Technique

The Support Vector anomaly detection method is a technique that is used to detect anomalies and is just as effective as the other methods. A support vector machine is in most cases linked with supervised machine learning. However, there are extensions (e.g. One-Class Support Vector Machine) that are used to identify anomalies in unsupervised machine-learning environments. These are environments where the training data is not defined or labeled.

This method is also known as novelty detection. It entails the automatic detection of abnormal or unforeseen phenomena. Examples of the unforeseen phenomena are the outliers that could be rooted in large data amounts.

3. Clustering-Based Anomaly Detection Method

The clustering-based anomaly detection is a very popular method. This is partly because clustering is one of the most used concepts in unsupervised machine learning.

In unsupervised learning, similar data points always tend to belong and aggregate in similar groups, called clusters. This is determined by the distance of these data points from the local centroids.

The k-means algorithm is very popular in clustering in unsupervised learning. It works by creating clusters of similar data sets. When this happens, there are data points that happen to fall outside these clusters and these are considered to be anomalies.

4. Supervised deep anomaly detection method

Typically, anomaly detection is treated as a problem involved in unsupervised learning. The supervised deep anomaly detection method is a technique where anomaly detection happens by making use of a trained deep supervised binary and using the labels for both the normal as well as the anomalous data instances. The deep supervised binary is also known as a multi-class classifier.

5. Semi-Supervised deep anomaly detection method

The semi-supervised deep anomaly detection technique is a more popular method than the supervised method. Here, labels are also used for both the normal as well as the anomalous data instances. However, in this method, the labels used for normal instances are way easier to obtain, making this a widely adopted technique in separating outlier data instances.

6. One-Class Neural Networks (OC-NN) Technique

The one-class neural network model is used in anomaly detection for complex data sets. Deep networks are known to have the ability to extract a representation of data that is progressively rich. This method combines this ability with the one-class objective of developing a tight cover around normal data.

The one-class neural network is a new method that has been customized to detect anomalies and is a departure from the anomaly detection approaches that use hybrid approaches.

Benefits

Using these methods, businesses can easily reap benefits. These machine learning techniques has some really great real-life, practical applications in the corporate setting. An array of different business processes can be accelerated or streamlined using these methods. Following are some of them:

Service failure: An organization can provide many different types of services. Such services might be in the form of an app, something much more concrete like a physical server. Whatever may the service be, there is always a chance of disruption in the workflow. These machine learning tools or something even more exotic along with them can be used to detect these failures early on.
Process control: Statistical Process Control (SPC) is a common term in the manufacturing process. Data related to the quality of the product is measured and retrieved during the manufacturing and checked whether the data is within the predetermined control limits. If the data is found to be outside the control limit, they would be considered as outliers or anomaly. Anomaly detection can be used in SPC for detecting any variation in the product or in the manufacturing process.
Cyber espionage detection: Any intrusion in an organization’s information database is bad news. The damage would be minimal if the database contains non-personal data. But if the database contents are confidential or sensitive, the damage can cause serious damage to the organization – both financially and socially.

Any such nefarious intrusion can cause a loss in the range of millions or even billions. Anomaly detection can prove to be an effective tool for countering such cyber threats. Anomalous activities could be detected much more easily and accurately. Such methods can be used effectively for solving and addressing issues in the information system.

Data harvesting: Anomaly and outlier detection plays a big role in data-centric industries like IoT. It also plays an important role in the process of data gathering and analyzing large-scale mobile data. IoT has become one of the key aspects that drive the lifestyle of modern humans. As gathering data with the help of mobile sensors have become much cheaper and easier than ever, anomaly detection in these large-scale data has become one of the biggest challenges.