In human decision-making, pattern recognition plays a key role in identifying objects or events involved in a situation. Pattern recognition happens largely unconsciously in the human brain (for instance, the process of recognizing faces), and it is effective in identifying only a small subset of acoustic and visual patterns.

However, the concept of pattern recognition can now be extended and implemented with computing systems. In fact, pattern recognition, a seemingly simple cognitive tool, is at the heart of the most advanced predictive maintenance systems. 

In the following section, see how pattern recognition is implemented in machine learning systems, followed by its applications in machine condition monitoring and maintenance.

What is pattern recognition? 

Pattern recognition is a process of data analysis which enables the identification of patterns concealed within the data. The pattern is identified based on a signature which uniquely correlates with a particular category or state represented by the input data. For instance, a face recognition algorithm is trained to accurately identify a particular face from a multitude of input images. 

Pattern recognition can be applied on any type of data – for example, image, audio, text, or even numeric and time series data. Pattern recognition is typically a part of an intelligent application, and is aided by other decision making techniques which utilize the output of pattern recognition algorithms.

Key pattern recognition techniques

Pattern recognition is not a recent development. In fact, the very first of pattern matching techniques, the Rabin–Karp algorithm, has been around since 1987. However, pattern recognition techniques have grown more and more effective and sophisticated since then, exploiting the increased computational power to implement more computationally expensive algorithms. Here are some of the most important pattern recognition techniques in use today.

Template matching

Template matching is the oldest pattern recognition technique, which simply compares two objects for similarity. The similarity is established through a reference template – typically an image or an audio pattern. Template matching is usually implemented to detect the presence of a pattern within a larger image, and simple implementations can be done with libraries like OpenCV.

The key drawback of template matching is that it is not very effective in detecting patterns where distortion or noise is present.

Statistical pattern recognition

Statistical pattern recognition makes use of a probabilistic framework to identify patterns in the input data. It uses probability distribution and density functions to look for patterns, which in this framework, are defined by statistically similar characteristics. Here, the data is represented as a collection of ‘vectors’ of numerical features. 

The key drawback of statistical pattern recognition is that it requires massive amounts of training data to gain accuracy, and can be slow to execute.

Syntactic and structural techniques

Structural pattern recognition techniques are used when the input data consists of clear, rather than approximate structures in patterns – for instance, in grammatical relations between linguistic elements. These techniques are useful for identifying sub-patterns within a pattern, and can describe objects present within the input data.

Neural network pattern recognition

Neural network (NN) based pattern recognition is one of the most advanced, and newest techniques available today. It makes use of neuron-like structures, consisting of simple processing units with multiple interconnections to spot patterns within input data. Unlike template matching techniques, which look for similarity between two objects, neural networks evaluate similarity by looking for features of a pattern in the input data. 

In NNs, the neuron can be fed with weights that determine the importance of a particular feature in identifying a pattern. By changing the weights over iterations, NNs can generally identify patterns with a higher degree of accuracy compared to other methods.

The purpose of pattern recognition in predictive maintenance

Pattern recognition approaches like statistical techniques and neural networks are essentially a subset of machine learning technology. In predictive maintenance applications, pattern recognition is used to observe the state of a machine, identify patterns that precede the machine’s failure, or underpin suboptimal performance.

For example, vibration analysis, which is applied in failure prediction of mechanical components like bearings, shafts, and gears, relies on pattern recognition. In vibration analysis, normal function of the machine corresponds to a pattern of vibration signals. As soon as the vibration signature deviates from the pattern underpinning normal function, the PdM system can be trained to trigger an alert for an upcoming failure. The greater the accuracy of the pattern recognition algorithm, the greater the confidence with which the prediction can be made.

Examples of pattern recognition in predictive machine maintenance

Predictive maintenance applications use a variety of techniques to evaluate machine health in real time. They utilize image data, acoustic signals, infrared imaging, or time series data for temperature, pressure, torque, voltage, or current – to forecast the likelihood of machine failure. These signals correspond to the state of the machine, and before a failure, these signals vary in a particular fashion. 

For instance, sudden spikes in temperature inside a cement kiln is usually the precursor to a refractory failure. These spikes can be detected with thermographic camera images captured from inside the kiln, using appropriate pattern matching techniques. Pattern matching algorithms are trained to learn the shape of a normal cycle (this is typically a sequence of features, or patterns of variation), and recognize deviations from them.

However, real-world applications of pattern matching in machine learning are more complex. This is especially the case with predictive maintenance applications, where the state of a machine is determined, not by a single variable or image feed (image time series) but by multiple streams of data (sometimes in the order of hundreds). Moreover, these data feeds typically consist of multiple data types and cardinality. In such application scenarios, machine learning techniques are crucial for accurate pattern matching. 

Key takeaways

Pattern matching is a key cognitive capability of intelligent systems. It enables these systems to see patterns in situations where humans cannot – for instance, in the vibration signals corresponding to normal functioning of a machine. These patterns can then be learnt by algorithms, and deviations from normal function can be caught by them to alert maintenance teams of an upcoming failure. 

In predictive maintenance applications, machine learning techniques are crucial to implement pattern recognition on hundreds of input parameters, which correspond to the machine state. Finally, the precision of the pattern matching algorithms plays a key role in the accuracy of predictions of the PdM solution.

Achieve industry-leading machine uptime outcomes with UptimeAI, a cutting edge PdM solution that leverages the most advanced pattern matching techniques to deliver high-confidence forecasts. Contact UptimeAI to improve plant operations and maintenance.