IoT 102: Implementing predictive analytics
Pedro Pereira Freelance writer and editor
November 30, 2022
The Internet of Things (IoT) has immense transformational potential. Just about any company can identify opportunities for IoT-driven changes across its operations. The trick is to decide where to begin.
As discussed in this IoT 101 blog, setting a clear vision for your IoT implementation is essential. You need a roadmap and a clear vision of the outcome. And that means deciding on a use case for the implementation.
Predictive maintenance is an increasingly popular use case in the industrial, manufacturing, and utility sectors, which depend heavily on equipment such as drive systems, large motors, and fleets of vehicles to run their operations.
Traditionally, organizations have tied their maintenance schedules to the calendar — or hours of usage — to determine when to service machinery. Some guesswork was involved. For instance, parts were replaced on a schedule to prevent a possible malfunction, even if there is no indication the part is about to fail.
With a predictive model, maintenance depends on precise knowledge of how the equipment is operating. Sensors placed on machines capture health and performance data, and then transmit it through the IoT for analysis. This leads to two types of action:
Real-time corrective action to prevent imminent failure
Long-term decisions to keep equipment operating optimally
The predictive approach extends equipment lifecycles and prevents downtime, saving organizations substantial amounts of capital and reducing their carbon footprint.
Real-time monitoring
Predictive maintenance hinges squarely on a simple precept: Determining what action to take in the future requires knowing what is happening now and an understanding of historical patterns.
It all starts with real-time monitoring. If you can see how well a machine is working on an ongoing basis, you can take immediate corrective steps when something goes wrong.
Data is collected from machines through sensors that detect vibration, internal temperature, or other raw data. Some sensors have threshold alarms built-in; when temperature, for instance, rises above the threshold, a technician is warned of a potential condition that may lead to a malfunction.
In some cases, this may involve shutting down equipment. While a shutdown incurs costs by halting production, the ability to take swift action prevents even bigger costs. A malfunctioning part in a larger piece of equipment can cause other parts to break.
Sensors can also pick up environmental conditions such as air temperature and humidity. Making adjustments to cooling systems to optimize these conditions helps extend the life of equipment by preventing issues such as overheating.
Ongoing data collection
The real-time component of data capture and analysis is critical because it enables immediate corrective action. It also puts the “predictive” in predictive maintenance.
Here’s how: Data captured from machinery is stored for historical analysis. Over time, the data can be analyzed with machine learning (ML) algorithms to identify trends, patterns, and indicators of potential failure. Eventually, the algorithms sift through enough data to enable a calculation of the remaining life span of a piece of equipment.
As such, operators can implement a training schedule based on machine health and forecasted life span. This model ensures that service and parts replacement occur only when necessary, as opposed to taking an imperfect best-guess approach. Maintenance can be scheduled around peak production times to minimize disruption.
With predictive maintenance, the amount of time dedicated to maintenance is reduced, the equipment lasts longer, and all associated costs decrease. Over time, as ML and IoT systems used in predictive maintenance get more sophisticated, many of the corrective responses to alerts can be automated. For instance, a system can learn to adjust temperatures on its own, or turn machinery on and off when necessary.
Navistar reduced vehicle maintenance costs by 30% with the help of IoT-enabled predictive maintenance.
Data management platform
To implement predictive maintenance successfully, organizations need a secure, scalable, cost-effective big data management platform such as the Cloudera Data Platform (CDP). The platform is popular with industrial, manufacturing, automotive companies, and utilities for its track record of reducing costs, enabling insights, and improving performance. In other words, the platform enables data-driven operations and data-centric automation—helping to transform organizations into digital powerhouses and bringing changes to our lives.
It’s not just about operational efficiency and automation. CDP and its self-service analytics tools help organizations empower users to leverage data, regardless of their data science or software engineering knowledge. This means, subject-matter experts gain first-hand access to data analytics and can provide invaluable feedback to predictive analytics models, improving the decision-making process.
Among its other functions, CDP ingests data in real time from a variety of structured and unstructured data sources. It also delivers real-time data processing and insights, ML capabilities and multiple analytical engines, including search and SQL analytics.
Learn how leading manufacturers are leveraging CDP and predictive analytics to improve their processes.
With an effective data management strategy in place, you can really jump-start your IoT implementation strategy, adding use cases to position your organization to thrive in the digital future.
Article by
Pedro Pereira
Pedro Pereira is a New Hampshire-based freelance writer and editor. He has covered the IT industry for more than two decades, focusing on cybersecurity, AI and IoT.