As the world of industrial technology has become more competitive, companies have realized the importance of being able to adapt quickly to unpredictable changes in workflow and production. When service-related problems occur and downtime is unplanned for applications or machines, companies risk losing money, customers and the ability to ensure that necessary deliverables are on time. Predictive maintenance, which uses machine learning (ML), is an approach that businesses can implement to avoid these service-related heartaches.
For example, in a manufacturing plant, ensuring all critical components function smoothly, such as those that monitor oil analysis, vibration analysis or temperature profile, is essential to prevent downtime. In automotive assembly or fabrication robotics, peace of mind comes from knowing that vital equipment is performing optimally. A single broken sensor in a manufacturing facility can derail production, causing businesses to miss critical deadlines.
By creating predictive maintenance models, engineers can predict equipment problems, minimizing downtime, improving employee safety and extending a machine’s lifetime. This not only saves costs but also enhances productivity and customer satisfaction.
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What is predictive maintenance?
Predictive maintenance is a strategy that leverages data on a machine’s historical performance. By analyzing the condition of a machine or production equipment, engineers can use machine learning to create self-training models that predict when a machine might break down or require repair.
Sensors play a crucial role in accurate predictive maintenance, as they capture and collect vital information about a machine’s current performance. Parameters such as temperature, vibration, sound profile, electric current, lubrication viscosity and more are measured and can be directly sent to engineers and predictive models for monitoring and iterative analysis. When any abnormality is detected, the system issues an alert, enabling management to address the issue before it escalates.
The internet of things has been crucial for predictive maintenance functionality because it allows data to be efficiently automated from sensors. Unlike manual data entry, which can be prone to errors, this automated information provides a reliable and secure real-time exchange between a user and a computer. The use of automated data for predictive maintenance has had immense benefits for industrial efficiency.
Reduction in downtime
Fixed schedules are necessary so that companies can maximize the number of products they are able to release and sell to customers. By leveraging ML and the power of IoT for predictive maintenance, programs can now intelligently analyze equipment health, enabling employees to optimize maintenance schedules and maximize uptime.
Operating efficiency
By continuously monitoring equipment health in real time, predictive maintenance empowers engineers to optimize maintenance strategies. This insight into potential failures, from impending mechanical issues to future outages, allows for prompt intervention and improved efficiency. This foresight also has the benefit of increasing safety measures for manufacturing employees by addressing potential safety risks or accidents before they happen.
Increasing productivity
Research from Siemens has reported that implementing predictive maintenance and anticipating failures could potentially reduce machine downtime by 50%, increase staff productivity by 55% and improve forecasting of potential machine downtime by over 85%—this is a win for overall reliability.
Building an effective predictive maintenance solution
When creating a predictive maintenance solution, it is important for engineers to first determine the sensor input. This is what is referred to as “time-series data.” In many instances, multiple environmental sensors can be deployed to strengthen the prediction algorithm. Different sensors provide insights into a machine’s health at different stages of failure.
Next, it is important to make an algorithm choice. Basic systems rely on a static threshold-based approach. An anomaly is triggered if a sensor surpasses a preset value limit.
In more advanced deployments, multiple condition thresholds can be set and varied dynamically in conjunction with one another. Most advanced systems rely on AI/ML techniques to train a model and supervise the operation. These algorithms can be configured or “trained” upon installation to determine a machine’s “health score,” which is much easier for humans to interpret.
Providing support and updates for the future
As a business grows, its predictive maintenance solutions need to scale to higher network volumes, more machine data payloads and more accurate sensing. Depending on the needs of a business, readily available algorithm examples, such as software and tools from AI/ML experts and partners for model training from solution providers such as Silicon Labs, can be a good fit.
The benefit of one of these options is that they come with support and help with any necessary updates so that data from machines can remain accurate and continually evolve. It also saves customers valuable time because they can base their developments on sample applications.
Eliminating network latency
Network stability is vital for a predictive maintenance installation to be beneficial. In many instances, machines are surrounded by magnetic disturbances or metal interference. Engineers could deploy a scalable self-repairing mesh network that is power-efficient yet allows for significantly large data payloads to be transmitted wirelessly without error.
Silicon Labs offers the MG24 series of multiprotocol wireless system-on-chips, as an example, which is well-suited for energy-efficient automation in smart factories, smart buildings and smart metering applications. The series has a strong, high-performing radio-frequency module for communication with other devices in its range.
Learning in real time
A predictive maintenance algorithm must continually adapt to detect all anomalies to be optimized for changes in its environment. For example, the ML capabilities on the MG24 comes with a matrix-vector processor engine for the AI/ML operations on the edge. This data collection can take place at any level, but by doing it locally on the edge rather than on the cloud, decisions can be made faster by eliminating the delay in uploading to the cloud and back.
This saves a tremendous amount of power and machine compute processing bandwidth. Furthermore, by doing these calculations on the machine itself, the system is not susceptible to network failure and is inherently more secure from cyberattacks.
The future of predictive maintenance and ML
Predictive maintenance and ML technologies are already being used throughout a wide variety of industries, and as more time passes, they could continue to prove their value in industries ranging from smart buildings to supply chains. Predictive maintenance can help engineers predict and prevent unplanned downtime that costs companies a lot of money, as well as help assess the integrity of infrastructure in factories so that people can remain safe.
The growth of the IoT and innovation in predictive maintenance will enable device makers to transform from reactive problem-solving to proactive optimization. By embracing a data-driven approach on the edge, we can build a more reliable and resilient connected world.
About the author
Tristan Cool is an industrial IoT product marketing manager at Silicon Labs. He leads the company’s exploration of alternative AI/ML applications for the IoT and leads the Industrial Asset Monitoring Segment, covering applications such as asset tracking, machine condition monitoring and fleet telematics.
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