First things first: what is predictive maintenance?
Predictive maintenance is a proactive approach to equipment upkeep that uses machine learning to foresee potential failures. This, in turn, helps prevent costly downtime.
Think of it as having your equipment talking, telling you exactly when it needs attention, just before something goes wrong.
A predictive maintenance system works like a team of inspectors watching over your equipment 24/7, collecting and crunching massive volumes of data in real time. These “inspectors” can detect the smallest anomalies and patterns that escape the human eye, warning you of potential failures before they become major headaches.
Now, let’s have a look at an example to see how the concept works in practice. Imagine a manufacturing plant that relies on a fleet of industrial robots to keep operations running. Traditionally, maintenance for these robots was scheduled at fixed intervals or, worse, when a breakdown actually happened, causing downtime and financial losses.
With machine learning predictive maintenance, equipment is being constantly monitored with the help of sensors strategically embedded into the machines. The data gathered can span temperature, vibration, power consumption, and other metrics.
All this data is then sent to the brain of the system powered by machine learning algorithms. Drawing on historical breakage data, repair history, environmental factors, and performance metrics collected in real time, the brain seeks out patterns that might signal an upcoming malfunction.
Armed with these valuable insights, your maintenance team can swoop in, carry out the needed fixes, and prevent the looming breakdown from ever happening. All that with minimum downtime and no costly disruptions.
And this is exactly where the cost-saving potential of machine learning predictive maintenance comes from. Due to its proactive nature, you can focus your efforts and budgets on the equipment that really needs attention — instead of wasting resources on catering for the equipment that is perfectly fine. On the other hand, with the power of foresight, you can act before a problem escalates, ensuring that your equipment operates at peak performance for longer periods.
Use cases of AI predictive maintenance across sectors
AI-driven predictive maintenance is fundamentally transforming the way equipment-dependent industries run. Below, we show examples showcasing the impact predictive maintenance can drive.
Manufacturing
In discrete and process manufacturing, AI predictive maintenance proves invaluable for a wide array of equipment, including milling machines, hydraulic presses, conveyor systems, robotic arms, and others.
Consider milling machines that shape raw materials into precision components. Equipping these machines with vibration sensors and analyzing sensor readings against historical failure data can help detect misalignment and signs of wear in machines’ spindles. And tracking temperature, you may catch signs of overheating signaling that a malfunction might be brewing.
The power of machine learning-based predictive maintenance doesn’t stop there. Hydraulic presses can be monitored for clogs and leaks that send a signal about malfunctioning. Additionally, through constant temperature monitoring, predictive maintenance solutions may detect signs of overheating, a red flag for impending issues within the hydraulic system. And with the ability to count cycles, machine learning predictive maintenance may accurately predict when specific components may require attention, enabling timely maintenance interventions and preventing potential breakdowns.
When it comes to conveyor belts, predictive maintenance software can measure belt tension to prevent premature wear. And by monitoring motor load, it may ensure efficiency and uncover any hints of trouble that could cause unexpected downtime.
Among manufacturers using predictive maintenance is Mercer Celgar, one of the largest pulp and solid wood producers. Predictive maintenance has become a key part of the company’s production strategy.
After exploring various options, Mercer Celgar implemented an off-the-shelf predictive maintenance solution. This AI-powered software seamlessly integrated with their new CMMS system, replacing the previous automated machine health diagnostic software. Through three pilots, the deployment yielded valuable insights and learnings.
With the AI-based predictive maintenance platform in place, Mercer Celgar gained the ability to monitor multiple production lines and obtain individual reports for each machine type within a single platform. This allowed them to track and compare performance while providing early insights to their maintenance teams regarding potential breakdowns and failures.
The implementation transformed Mercer Celgar’s operations, creating specific job roles and centralizing machine performance data from multiple sources into one platform. As a result, they have a single source of truth for machine performance, facilitating efficient monitoring and data processing at scale.
Automotive
The automotive industry heavily relies on machine learning-based predictive maintenance to enhance reliability and reduce warranty costs. By monitoring sensor data from vehicles in the field, for example, automakers can detect potential issues before they become critical failures.
Let’s consider a fleet of delivery trucks. Through continuous monitoring of engine performance, tire condition, and fuel efficiency, predictive maintenance enables companies to optimize maintenance schedules, prevent breakdowns, and extend the lifespan of their vehicles.
A number of automotive companies have turned to predictive maintenance to anticipate maintenance opportunities and retain customers, encouraging them to seek maintenance services from the car manufacturer’s official network. One such company is Opel, which has equipped its vehicles with a predictive maintenance tool that analyzes historical data to suggest optimal inspection schedules.
As the main objective of implementing the system is to improve customer retention, the system notifies drivers when maintenance is due and arranges appointments with Opel’s official network, discouraging customers from turning to third-party repairers.
Healthcare
Machine learning predictive maintenance is revolutionizing the healthcare industry, ensuring the smooth operation of critical medical equipment. Picture an MRI machine in a hospital. By analyzing sensor data, including temperature, pressure, and electromagnetic field strength, predictive maintenance algorithms can detect anomalies, deviations, and patterns indicative of potential issues. Hospitals can then proactively schedule maintenance activities during low-demand periods, minimizing disruptions to patient care. This approach leads to improved operational efficiency, reduced equipment downtime, and enhanced patient outcomes.
One healthcare organization to embrace such an approach is Kashiwa Health Check Clinic. They’ve partnered with Hitachi to implement a predictive maintenance system to prevent MRI machine failures. The technology partner analyzed three years’ worth of sensor data from 100 MRI systems and created a mechanism to investigate the cause patterns that lead to device failures. Machine learning then helped to define a normal operational state, foresee abnormalities, and detect changes that lead to failures. As a result, the organization reduced MRI machine downtime by 16.3%.
Oil & gas
The oil and gas sector, known for its intricate and expensive equipment, benefits immensely from machine learning-based predictive maintenance. Imagine an offshore drilling rig with numerous pumps, compressors, and turbines. Through continuous monitoring of parameters like pressure, temperature, and flow rate, predictive maintenance algorithms can detect anomalies and predict impending failures. By taking a proactive approach, companies can avoid costly unplanned shutdowns, optimize equipment performance, and ensure the safety of personnel.
Aramco, a leading player in the manufacturing industry, has harnessed the power of predictive maintenance to prevent corrosion, scaling, and other damage of equipment and pipelines caused by excess water coming from wells together with oil and gas. Having implemented an AI-powered flow meter, the company gained a real-time insight into water flow in the wells. This meter employs an AI-powered digital twin model to anticipate pipe flow and then compares it with the actual measurements. This process enables the meter to automatically adjust itself across various operating conditions, greatly minimizing recalibration requirements.
How to build a predictive maintenance solution?
When it comes to adopting machine learning-based predictive maintenance solutions, organizations face a decision: opt for off-the-shelf software or invest in a custom-built solution. Each approach offers distinct advantages and considerations, and the choice depends on several criteria:
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Available budget: Off-the-shelf software (e.g., IBM Maximo, Microsoft Azure IoT Suite, GE Predix, PTC ThingWorx, Siemens MindSphere, and others) generally comes with lower upfront costs, while building a custom solution can involve a higher initial investment. Companies with limited budgets may find off-the-shelf software more appealing, while those with substantial resources might lean towards custom solutions for tailored functionality.
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Time-to-implementation: Off-the-shelf software can be quickly deployed since it is pre-built and tested. In contrast, developing a custom solution may take longer, depending on the complexity and scope of the project.
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Industry-specific requirements: Different industries often have unique needs and workflows that require predictive maintenance solutions to have specific features. Off-the-shelf software does not fully align with these requirements and needs customization. So, businesses with highly specialized needs or those seeking a competitive edge through tailor-made solutions may prefer to go the custom route.
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Existing infrastructure and integrations: Integrating predictive maintenance software with existing systems and equipment is critical for seamless operations. Off-the-shelf solutions may offer better compatibility with a limited number of popular pieces of equipment or technologies, while custom solutions can be precisely tailored to fit into an organization’s established infrastructure.
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Scalability: As businesses grow and expand, their predictive maintenance needs may evolve. Off-the-shelf software may provide scalable options that can be adapted to changing requirements. When properly designed, custom solutions can also offer scalability and flexibility, but this might require additional development effort.
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Support and updates: Off-the-shelf software often comes with vendor-provided support and regular updates, ensuring the solution remains up-to-date. Custom solutions may require ongoing maintenance and support efforts to ensure continued functionality.
Building an AI-based predictive maintenance solution from scratch
Building a custom machine learning-based predictive maintenance solution from scratch provides maximum flexibility and allows you to tailor the system precisely to your unique needs. Still, custom software is rarely fully custom. There are a number of open-source libraries and frameworks (e.g. Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost, etc.), SDKs and APIs (e.g., AWS SDKs, Azure SDKs , Google Cloud SDK, IBM Watson SDK, ThingWorx SDK, etc.), as well as fully managed machine learning services including pre-trained models (e.g., Amazon SageMaker, Azure Stream Analytics) that speed up and simplify the process of developing custom predictive maintenance solutions.
Below, we share our experience operating as an ML consulting company and explain how the process of developing AI-based predictive maintenance software may unfold.
Predictive maintenance is a task that can be divided into two sub-tasks: condition monitoring and predictive analytics. So, crafting a predictive maintenance solution essentially boils down to training a detection model and a prediction model and integrating them into a full-fledged solution. To train such models, you go through the following steps:
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Data collection
When crafting a predictive maintenance algorithm, the first step is to gather sufficient data for training the models. The types of data to collect span:
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Sensor data that gives real-time insight into the health and performance of machines. It includes time-series information collected from all types of sensors (e.g., temperature, pressure, vibration, voltage, noise, and more) attached to machines.
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Failure data that spans information about past equipment failures, including the date, time, and nature of the failure.
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Maintenance records, service reports, work orders, and other documentation related to the equipment’s maintenance history.
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Operational data that captures information about a machine’s operational status, including start and stop times, production rates, throughput, and usage patterns.
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Environmental data that spans factors such as ambient temperature, humidity levels, and other external conditions that may impact equipment’s health.
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Other relevant data that may provide additional insight into failure patterns, say, energy consumption or maintenance cost data.
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Data processing
Data preprocessing is an essential step in building a custom AI-driven predictive maintenance solution. It involves a range of techniques, from handling missing values to removing outliers and beyond.
The choice of the appropriate processing techniques depends on the characteristics of your data, while the key to successfully carrying it out is knowing your machines. Suppose you have noisy vibration data. Knowing the frequency range that contains markers of failure can guide you in choosing the optimal preprocessing strategy.
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Feature engineering
Feature engineering means transforming raw data into features that help distinguish between normal and faulty behavior. The goal is to extract relevant information from the data, making it easier for machine learning algorithms to identify patterns and relationships.
For example, you can monitor the condition of a gearbox using vibration data. Malfunctions of a gearbox result in changes in vibration frequency and magnitude. Peak frequency and magnitude can, thus, be used for monitoring gearbox health.
Some common techniques used in feature engineering span:
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Aggregation: Combining multiple raw data points to create aggregated features can provide a higher-level view of the equipment’s behavior.
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Time-based analysis: Time series data often contains valuable temporal patterns. Creating time-based features like time of day, day of the week, or seasonal patterns can help the model capture recurring behaviors.
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Signal analysis: Applying signal processing techniques can help extract frequency components or detect patterns in time series data.
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Correlation analysis: Assessing which data impacts the accuracy of prediction the most and eliminating those data points that have less significance when it comes to model training from the data set helps cut the time it takes to train them.
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Domain-specific feature extraction techniques: In some cases, domain knowledge can lead to specialized feature transformations. For example, for rotating equipment, features based on vibration order analysis can be useful.
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Feature engineering is likely to be a process that would involve multiple iterations. It is common to get back to this step after an initial round of training and testing a predictive model for optimal performance.
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Model selection and training
As the next step, you choose an optimal machine learning model and train it on the collected data.
Model selection
Choosing the right machine learning algorithm or predictive model is essential for your specific predictive maintenance use case. Different models have varying strengths and weaknesses, and the selection depends on factors like the nature of the data, the complexity of the problem, and the desired level of interpretability.
The models commonly used for predictive maintenance include:
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Logistic regression: Suitable for binary classification problems, for instance, predicting whether a piece of equipment will fail or not.
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Decision trees: Effective for handling both categorical and numerical data; can capture nonlinear relationships between features.
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Random forest: An ensemble method that combines multiple decision trees for improved accuracy and robustness.
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Support Vector Machines (SVM): Useful for classification tasks, especially when dealing with high-dimensional data.
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Model training
Model training involves using historical data to teach the machine learning predictive maintenance model to recognize patterns and relationships between features and the target variable, like equipment failure. The dataset is divided into two parts: the training set and the testing set.
The model learns from training data and becomes capable of making predictions based on the patterns it identifies during the training process.
Once trained, the model is put to the test on the testing data set. Model validation is a critical step to evaluate how well the model performs on unseen data and to estimate its predictive power.
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Integration and deployment
Once you’ve identified the right model, trained it, and generated accurate predictions, it’s time to put it into action by integrating it into your system. Depending on the specifics of your machine learning predictive maintenance software, you have several deployment options:
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Cloud implementation: Opt for a cloud-based deployment if you’re dealing with large volumes of data stored in the cloud. By running the machine learning algorithms directly in the cloud, you eliminate the need to transfer data back and forth between local machines and the cloud for prognostics and health monitoring. This streamlines the maintenance process and enhances efficiency.
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Edge implementation: Alternatively, you can run the algorithm on embedded devices located closer to the equipment. This approach allows immediate updates and notifications about equipment health without any delays.
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Hybrid implementation: You can also go for a combination of cloud and edge. Utilize embedded devices for preprocessing and feature extraction and run the predictive model in the cloud. This way, you get the best of both worlds — the efficiency associated with edge deployment and cloud-based processing power. It works particularly well for systems like oil drills or aircraft engines, where continuous operation generates vast amounts of data, making onboard storage or transmission challenging due to bandwidth and cost limitations.
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Challenges to address on the way
Implementing an AI predictive maintenance solution may come with certain challenges. Some common obstacles include:
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Legacy equipment: Connecting and collecting data from older or incompatible equipment can be a challenge. Retrofitting sensors or utilizing IoT gateways may be necessary to gather the required data.
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Establishing correct parameters: Identifying the most relevant parameters to monitor for each piece of equipment is crucial. This requires a deep understanding of the machinery and its failure modes to ensure accurate predictions.
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Data quality and availability: Ensuring data quality and availability is critical for accurate predictions. Incomplete or unreliable data can lead to incorrect maintenance decisions. Establishing data governance practices and investing in data infrastructure are essential steps to overcome this challenge.
To sum it up
AI-driven predictive maintenance offers a game-changing opportunity for companies operating in equipment-heavy or equipment-dependent industries. By embracing the proactive approach to maintenance, organizations can significantly reduce downtime, optimize maintenance costs, and enhance operational efficiency.
Whether by customizing off-the-shelf software or building a custom solution from scratch, machine learning predictive maintenance can help you to unleash the full potential of your equipment.
