Driving IoT Enabled Predictive Maintenance

Categories: IoT / Connected Products

Predictive Maintenance in the industrial worldIoT

Powered by sensors, connectivity and smart machines, the Internet of Things (IoT) is reshaping the manufacturing and industrial processes, effectively changing the paradigm from ‘repair and replace’ to more of ‘predict and prevent’. For asset heavy industries, unplanned equipment downtime can mean big losses in revenues and productivity. For example some of the leading automotive manufacturers estimate that unplanned downtime can cost them as much as $15,000 – $20,000 per minute and a single downtime event can cost approximately $2 million. Given the business impact, it is not surprising that these industries have been focusing on driving predictive maintenance to minimize downtime and losses.

Manufacturers and organizations in other asset heavy verticals cannot afford to wait till a machine or equipment breaks down in order to figure out what went wrong. On the other hand, enterprises also don’t want to spend costly time and resources doing unwanted maintenance to all of their equipment and machinery without really needing to do so.

IoT as the key enabler to drive Predictive Maintenance

Today, IoT and connected devices are essentially transforming the predictive maintenance landscape. Machines and equipments are outfitted with sensors that continuously monitor key attributes or performance indicators — such as temperature, pressure, vibrations/second, noise levels, etc. By capturing and utilizing data streaming from sensors and connected devices, businesses can now gain visibility into the condition of their valuable assets and specific components in real time.

Utilizing IoT and sensor data from connected equipments, they can effectively predict when and how an asset might fail, detect variances, understand warning signals, and quickly identify any patterns that might indicate a potential breakdown. Data-centric organizations are using advanced analytics and machine learning to detect anomalies or patterns that are indicative of failure and intervene as soon as initial signs of failure are detected to perform the right maintenance activities. Early detection – is the key to reducing failures and costs.

However, organizations often struggle to keep up with the massive volumes, variety, and velocity of data that these connected machines generates. Data streaming from sensors quickly adds up and managing petabytes of data from millions of sensors, and driving analytics on data in real-time, is one of the biggest challenges organizations face when it comes to successfully implementing IoT use cases.  

Cloudera – The Data Management Platform for IoT

More and more organizations in asset-heavy industries are utilizing Cloudera’s Enterprise Data Hub (EDH), powered by leading open source technologies, as the data management and machine learning platform to effectively drive predictive maintenance and other key IoT use cases. With Cloudera Enterprise, organizations can easily bring together sensor data along with data from diverse manufacturing systems and multiple sources into a single, unified platform at considerably lower cost. Since it is built on a highly scalable and flexible file system, any type of data—including structured data from manufacturing systems or historians or streaming data from sensors—can be loaded into the platform without altering its format, in order to drive machine learning and analytics to predict failures.

Predictive Maintenance

Organizations can drive real-time analytics on all their IoT data, including both data-in-motion and data-at-rest, regardless of where it lands — on the edge, on leading cloud platforms, on-premise, or in a hybrid mode, while ensuring industry leading security and compliance. Some of the key attributes of Cloudera Enterprise that are critical for IoT use cases include:

  • Real-Time Analytics: Enables real-time data processing on streaming data using in-memory processing engine, Apache Spark and Spark Streaming, supported by storage options like Apache HBase and Apache Kudu.
  • Machine Learning Capabilities: Provides out-of the-box machine learning libraries with Apache Spark that enable organizations to easily build predictive models and continuously iterate on them.
  • Enable Data Science for the Enterprise: Accelerate exploratory data science at scale and build machine learning models using Cloudera Data Science Workbench by taking advantage of massively parallel compute and expanded data streams.
  • Deployment Flexibility – Hybrid cloud. On-premises. Or both: Analyze all IoT data regardless of where it lands—on the edge, on leading cloud platforms, on-premises, or in a hybrid mode.

Predictive Maintenance—Customer Use Cases

Today Cloudera is powering a number of use cases on predictive maintenance across a diverse set of industries. Below is a summary of some of the use cases that highlights how some of our customers are utilizing Cloudera and the power of data analytics in IoT to drive predictive maintenance.

Setting Use Cases Customer Case Study—Description
Automotive Predictive Maintenance – Connected Vehicles One of the leading auto manufacturers in North America is using Cloudera as its data management platform to monitor the health of 300,000+ trucks in real time in order to improve uptime and reduce fleet maintenance costs by 40 percent.
Manufacturing Predictive Maintenance – Industrial IoT A leading industrial automation and robotics company is utilizing Cloudera to ingest, store, and analyze streaming sensor data from thousands of industrial robots, in real time, in order to eliminate machine downtime.
Heavy Machinery Predictive Maintenance – Heavy Machinery One of the biggest heavy equipment fleet manufacturers in North America is using Cloudera to parse large-volume and high-velocity data from sensors to continuously monitor performance of their fleet and to do predictive maintenance as well as advanced defect detection.
Buildings/ Airports Predictive Maintenance – Smart Buildings One of the busiest airports in Europe is running Cloudera on Azure to capture, secure, and correlate sensor data collected from equipment within the airport (e.g., escalators, elevators, and baggage carousels) to prevent breakdowns and improve airport efficiency and passenger safety.
Ports Predictive Maintenance – Smart Ports A leading provider of cargo-handling solutions is utilizing Cloudera to ingest and process IoT data that is streaming from sensors in port terminal machinery, including cranes and cargo-handling equipment, to improve operational efficiencies and increase uptime.

For more details, please check out these resources:


Leave a Reply