Predictive Maintenance Technologies

Predictive Maintenance adoption in industry has been enabled by the democratisation of asset data, connected by the IoT and the Cloud. It's now easier than ever for businesses to leverage data to realise value for the company and their customers.

The extent to which data is being produced in industry has increased the awareness of the core technologies behind Predictive Maintenance. Here, we will break down these technologies for you – and highlight the core aspects required to bring any company into Industry 4.0 where it can leverage the power of data driven insights.

Internet of Things (IoT)

Predictive Maintenance starts with the automated collection of data. The Internet of Things (IoT) enabes this by connecting your equipment to a central data collector, where telemetry data is uploaded. This data collector is often on premise or in the cloud (AWS, Azure, GCP, etc). The type of data that is collected can range from sensor information (temperature, torque, gear position, shear stress) to metadata such as boot count, the system's location, or the current number of elapsed cycles.

The assets of many organisations may be digitally native by design, meaning they have the ability to collect their own sensor data and upload it to a central data warehouse. Some assets may produce the derived insight from the raw sensor data on the edge, and then upload just this insight to the data warehouse. Some equipment, particularly large industrial machinery, would need to be retrofitted with sensor devices that collect data and send it to a central hub.

In any case, the IoT is a core enabling technology for Predictive Maintenance. The next core component to derive information from the sea of data created by IoT connectivity, is the application of Machine Learning and AI technologies.

Artificial Intelligence and Machine Learning

Modern predictive maintenance relies heavily on Machine Learning and AI technologies to construct conditional indicators and RUL models from the data collected in your IoT infrastructure. In reality, many classical and modern techniques sit under the context of machine learning. For example:

• Neural Net architectures
• Deep Learning
• Computer Vision
• Numerical Optimisation
• State Estimation Methods
• Frequency Analysis
• Time series Analysis
• Monte Carlo Methods

These methods can all be shaped into the Machine Learning paradigm – in practice they leverage modern computational power to derive insight from data.

Artificial Intelligence is closely related to machine learning but can be more accurately described as the combination of Machine Learning methods to produce system architectures that mimic intelligent perception. A perfect example of this are algorithms that can identify the qualitative nature from images; e.g. whether a part is defective on the manufacturing line, to whether a heat signature from a motor is typical or not. For this reason, AI methods can be extremely powerful in producing condition indicators from data if they are trained with domain knowledge.

Further to this, AI methods are used extensively in detecting anomalies from data and either regressing the data to values or classifying the observed parameters into classes. Additionally, classification of data into failure states and/or recognising patterns in the data that are indicative of failure, are all techniques that are possible with modern AI methods. For this reason, AI has a strong foothold in the application of Predictive Maintenance in industry.

Big Data and Cloud Computing

Implementation of algorithms that have been developed for predictive maintenance strongly rely on the advancements in Cloud Computing and Big Data. Any collection of IoT systems produces a lot of data, often a side effect in many industries by the need to store and archive data for regulatory reasons. An understanding of how to manipulate this data at scale and derive the information necessary for the PdM pipeline is where cloud and edge computing come in.

Cloud computing in Predictive Maintenance refers to the implementation of data processing software on off-premise servers. At the first stage there is the data collection from IoT sensors. The raw data is often passed through a processing pipeline which checks for correctness and manages any necessary formatting. The raw data is then stored in a data lake, ready to be served to processing engines that produce the Conditional Indicators and RUL models. Collection of the unstructured data into a form that is ready to be fed into an analytics pipeline is commonly done with an ETL (Extraction, Transform and Load) pipeline. Through this, more refined data can then be passed to the set of algorithms that generates the condition indicators and RUL models.

Where does Big Data fit in? Big data simply refers to the scale at which unstructured data comes into processing pipelines. The sheer scale of information that needs to be derived from such an amount of data has forced cloud computing architectures to be designed so that they can handle this volume without wavering. One method for handling increasing amounts of data is the Serverless paradigm, where cloud infrastructure is built on microservices that can scale with the amount of data being processed.

This information at scale does not come for free. Huge amounts of data require large processing pipelines and cloud computing bills can become unmanageable. To combat this, more industries are shifting portions of the processing pipeline onto the edge. This simply means that data insight creation is being performed at the time of generation. In this paradigm only the high level abstraction needs to be sent to the data collector, thus saving on significant amounts of data storage and processing time.

Additionally, within the past years, there has been a shift away from microservice architecture towards more monolithic approaches and ELT processing methods. ELT standing for Extract, Load then Transform. Such methods tend to modify the processing tradeoff betwen accuracy, availability and cost towards less availabilty and therefore lower cost, all whilst maintaining data accuracy.

Embracing Industry 4.0 through Predictive Maintenance

Predictive Maintenance is a powerful tool that leverages cutting-edge technologies to drive efficiency, reduce costs, and enhance operational reliability. By harnessing the power of the Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning, Big Data, and Cloud Computing, businesses can unlock unprecedented insights into their asset performance and make data-driven decisions to optimise maintenance strategies.

As the world continues to embrace Industry 4.0, the adoption of Predictive Maintenance becomes increasingly crucial for companies seeking to stay competitive and future-proof their operations. By proactively monitoring asset health and predicting potential failures, organisations can minimise unplanned downtime, extend equipment lifespan, and allocate resources more effectively.

Moreover, the integration of edge computing and evolving cloud architectures promises to streamline data processing, reduce costs, and enhance the scalability of Predictive Maintenance solutions. As these technologies continue to mature, the potential for even more advanced and efficient Predictive Maintenance strategies will continue to grow.

Ultimately, Predictive Maintenance represents a paradigm shift in the way businesses approach asset management, offering a data-driven, proactive approach that maximizes operational efficiency, reduces risks, and drives long-term value creation. By embracing this technological revolution, companies can position themselves at the forefront of Industry 4.0 and unlock a competitive edge in an increasingly digital world.