A Guide to Predictive Maintenance & Machine Learning

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Various economic pressures on businesses have created a focus on new and innovative ways to manage operational costs. At the same time, businesses are looking at using IT to help manage overall business costs and increase income—for example, by supporting remote working, and in many cases, enabling e-commerce to replace closed retail outlets.

Management of infrastructure to minimize downtime has two major benefits: reductions in support and maintenance costs and improvements in service levels. That is where predictive maintenance comes in and why many organizations have included it in their strategies.

One of the major stumbling blocks in the development of predictive maintenance was the resources required to store and analyze the large volumes of data collected on a regular basis.

However, recent advances in hardware technology have helped by bringing forward improvements in processing power and storage capacity, such as big data, AI, IoT, and cloud computing. Software solutions have also improved the quality and timeliness of analyses.

This post addresses the need for predictive maintenance and machine learning in cost management and how one goes about it.

What Is Predictive Maintenance?

Predictive maintenance is an approach that uses time series data and machine learning techniques to predict when maintenance should be performed on machinery or equipment. By leveraging historical data, sensor data, and advanced analytics, predictive maintenance aims to identify potential failures or issues before they occur, allowing for proactive maintenance actions and minimizing downtime and costly repairs.

What Is Machine Learning?

Machine learning is a subset of AI centered on developing algorithms and statistical models to help computer systems learn and make predictions or decisions without being explicitly programmed. It involves the use of data and iterative learning processes to automatically improve performance on a specific task.

In traditional programming, a programmer writes explicit instructions for the computer to follow. However, in machine learning, instead of providing explicit instructions, the computer is trained on a dataset to learn patterns, relationships, and rules that allow it to make predictions or take actions.

What Is Time Series Data?

Time series refers to a sequence of data points or observations that are collected and recorded over regular intervals of time. It is a popular data format in various domains, including predictive maintenance, economics, finance, weather forecasting, stock market analysis, and many other fields.

Because of the massive amounts of data in a large infrastructure, organizations routinely use machine intelligence to analyze it. Looking specifically at the physical infrastructure, tools such as InfluxDB have been developed to store and analyze data sourced from devices attached to the network.

Benefits of Predictive Maintenance With Machine Learning

Predictive maintenance offers several benefits for businesses:

  1. Increased uptime: By monitoring equipment proactively, predictive maintenance helps to identify potential failures or issues before they occur. This enables organizations to schedule maintenance during planned downtime, minimizing unexpected breakdowns and maximizing equipment availability.
  2. Cost savings: Predicting maintenance needs allows for better planning and resource allocation. By addressing maintenance issues before they escalate into major failures, businesses can avoid costly emergency repairs and reduce unplanned downtime. This also helps in optimizing the use of spare parts and minimizing inventory costs.
  3. Improved productivity: When equipment is operating optimally and breakdowns are minimized, productivity is enhanced.
  4. Enhanced safety: Equipment failures can pose safety risks to employees and the surrounding environment. Predictive maintenance helps identify potential hazards early on, allowing for timely intervention and reducing the likelihood of accidents or incidents.
  5. Extended equipment life span: Regularly monitoring and maintaining equipment based on predictive insights can extend its useful life.
  6. Efficient maintenance planning: Predictive maintenance leverages data analytics and machine learning algorithms to analyze patterns and trends in equipment performance. This allows businesses to optimize maintenance schedules, prioritize tasks, and allocate resources effectively. By streamlining maintenance operations, organizations can reduce idle time, minimize overtime costs, and improve overall maintenance efficiency.
  7. Data-driven insights: Predictive maintenance generates a wealth of data about equipment performance, failure rates, and maintenance activities. By analyzing this data, businesses can gain valuable insights into equipment reliability, root causes of failures, and opportunities for process improvements.

Implementing machine learning for predictive maintenance

Implementing predictive maintenance with machine learning can bring considerable benefits to an organization. However, realizing these benefits will depend on the software and machine learning model chosen. Here are a few pointers on how to go about it.

Software Selection

Selecting the right predictive maintenance software involves considering various factors such as your specific requirements, budget, integration capabilities, scalability, and ease of use. Here are some common considerations in assessing predictive maintenance applications like InfluxDB:

  1. Using AI and machine learning to monitor asset health, predict failures, and optimize maintenance schedules.
  2. Applying machine learning algorithms to analyze data from sensors and other sources to detect anomalies, predict failures, and schedule maintenance activities.
  3. Employing tools for building and deploying predictive models. The combination of these essential services allows for real-time monitoring, anomaly detection, and predictive maintenance.
  4. Ensuring scalability. It is important that the chosen solution can support new devices and scale up to meet increasing demands without needing major investments in new hardware or software.
  5. Ensuring ease of integration. The software should be capable of direct connectivity or support importing and exporting data to and from other applications. This is particularly important for cloud-based solutions.

Consider your organization’s specific needs, available data sources, integration requirements, and budget when selecting predictive maintenance software. It’s often beneficial to evaluate multiple options, conduct proofs of concept, and involve stakeholders from different departments (such as maintenance, operations, and IT) to ensure the chosen software aligns with your goals and can deliver actionable insights for optimizing maintenance practices.

Machine Learning Considerations: Model Selection

Predictive maintenance with machine learning uses machine learning models to analyze incoming data and realize the benefits of predictive maintenance. The model chosen will depend on the business area in which predictive maintenance is being used and the results required.

In short, machine learning models are mathematical representations or algorithms that are trained on data to make predictions or take action. Here are some commonly used machine learning models:

  1. Linear regression: Linear regression is a basic and widely used model for predicting a continuous output variable based on one or more input variables.
  2. Logistic regression: Logistic regression is used for binary classification problems in which the goal is to predict one of two possible classes.
  3. Decision trees: Decision trees are hierarchical structures that make decisions based on a sequence of rules. They divide the input space into regions and assign a class label or value to each region. Decision trees are interpretable and can handle both categorical and continuous input variables.

Although these are the most common, there are many more machine learning models you can use based on your needs.

Depending on the problem at hand, different models may be more suitable. There is a wide variety of algorithms and techniques available, each with its strengths and limitations. The choice of model depends on factors such as the nature of the data, the task at hand (classification, regression, clustering, etc.), interpretability requirements, computational resources, and the desired level of accuracy.

Implementing Machine Learning

  1. Model selection: Choose the appropriate machine learning algorithm(s) based on your specific problem and available data.
  2. Model training: Split the data into training and validation sets. Use the training set to train the machine learning model(s) on the labeled data. Optimize the model hyperparameters and evaluate its performance using the validation set. Iteratively refine the model until you achieve satisfactory results.
  3. Model deployment: Once you train and validate the model, deploy it in a production environment. This could involve integrating it with existing monitoring systems, data pipelines, or IoT platforms to collect real-time data from sensors and trigger maintenance actions based on predictions.

Final Thoughts

It’s important to note that implementing predictive maintenance using machine learning is an iterative process that requires domain knowledge, collaboration between data scientists and subject matter experts, and a robust data infrastructure. Regular maintenance and data quality assurance are also crucial to ensure the accuracy and reliability of the predictive maintenance system.

This post was written by Iain Robertson. Iain operates as a freelance IT specialist through his own company, after leaving formal employment in 1997. He provides onsite and remote global interim, contract and temporary support as a senior executive in general and ICT management. He usually operates as an ICT project manager or ICT leader in the Tertiary Education sector. He has recently semi-retired as an ICT Director and part-time ICT lecturer in an Ethiopian University.