- Community Home
- >
- Solutions
- >
- Tech Insights
- >
- Prescriptive maintenance data requirements: It’s m...
Categories
Company
Local Language
Forums
Discussions
Forums
- Data Protection and Retention
- Entry Storage Systems
- Legacy
- Midrange and Enterprise Storage
- Storage Networking
- HPE Nimble Storage
Discussions
Discussions
Discussions
Forums
Discussions
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
- BladeSystem Infrastructure and Application Solutions
- Appliance Servers
- Alpha Servers
- BackOffice Products
- Internet Products
- HPE 9000 and HPE e3000 Servers
- Networking
- Netservers
- Secure OS Software for Linux
- Server Management (Insight Manager 7)
- Windows Server 2003
- Operating System - Tru64 Unix
- ProLiant Deployment and Provisioning
- Linux-Based Community / Regional
- Microsoft System Center Integration
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Discussion Boards
Community
Resources
Forums
Blogs
- Subscribe to RSS Feed
- Mark as New
- Mark as Read
- Bookmark
- Receive email notifications
- Printer Friendly Page
- Report Inappropriate Content
Prescriptive maintenance data requirements: It’s more than IoT
Andy Longworth, HPE Pointnext
In manufacturing, new technologies are changing the way companies maintain their equipment to achieve better efficiencies and limit downtime. It’s called prescriptive analytics, and it uses IoT sensors, AI, and processing at the edge to help operators and engineers make better decisions about production equipment on the factory floor.
Maintenance of factory machinery is critical to keeping plant staff safe, maintaining output, reducing unplanned downtime and defects, and preserving the value of capital expenditures. Manufacturers have traditionally been reactive when it comes to maintaining machinery—waiting for something to break or show signs of wear—or performing maintenance on a fixed schedule. Both approaches are terribly inefficient and costly.
Prescriptive analytics looks deep into the data coming off individual assets or connected systems and tries to identify patterns or anomalies that indicate problems or looming outages. The data and analysis can point to the origin of the error and its potential impact. This informs human operators about whether they need to make a change right away, or if the issue can wait until the end of the shift to be remedied. Ultimately, the goal is to minimize the impact on the business, and hopefully make better use of available resources.
Data requirements for prescriptive analytics
Data lies at the heart of prescriptive analytics. Companies considering a pilot have to understand what data they need, what data the machinery or IoT systems can provide, and how the data can be transformed into a format that the analytics application can use.
An AI needs lots of data to be trained. Tens or even hundreds of thousands of samples or data points might be required to teach the AI the difference between optimal and sub-optimal performance states for an individual CNC machine, robot, or pump. That data has to be available at the edge of the network, where the equipment is installed, to be processed and analyzed without having to incur bandwidth costs or potential outages. Once the system has modelled what falls within the range of normal behavior and what's considered abnormal, it can start to predict failures.
The prescriptive analytics AI can also be trained to inform operators what’s causing the problem. If sensors on a milling machine detect excessive vibration, is that caused by bearings, an imbalance, or something else? This information can also be tied into criticality within the production line. If the milling machine has a buffer and the cause is a minor criticality, it may be possible to sustain an outage for an hour or two. If it’s a major criticality, the production line may need to be immediately shut down to address the issue.
You can use the AI aspect of prescriptive analytics to understand what's happening to a piece of equipment, but you also need to consider the business data around it. Understanding where a particular piece of equipment sits within the production process and what ties into it is crucial. What are the maintenance schedules, and who is responsible? Where are parts sourced from? How will the shift pattern be impacted? These kinds of business data, as well as the data around the equipment you are monitoring on the edge, allow operators to proactively maintain plant equipment – and keep production lines running.
Featured articles:
- Cities push predictive analytics to combat social ills
- Predictive analytics in the multicloud
- Want to know the future of technology? Sign up for weekly insights and resources
- Back to Blog
- Newer Article
- Older Article
- Amy Saunders on: Smart buildings and the future of automation
- Sandeep Pendharkar on: From rainbows and unicorns to real recognition of ...
- Anni1 on: Modern use cases for video analytics
- Terry Hughes on: CuBE Packaging improves manufacturing productivity...
- Sarah Leslie on: IoT in The Post-Digital Era is Upon Us — Are You R...
- Marty Poniatowski on: Seamlessly scaling HPC and AI initiatives with HPE...
- Sabine Sauter on: 2018 AI review: A year of innovation
- Innovation Champ on: How the Internet of Things Is Cultivating a New Vi...
- Bestvela on: Unleash the power of the cloud, right at your edge...
- Balconycrops on: HPE at Mobile World Congress: Creating a better fu...
-
5G
2 -
Artificial Intelligence
101 -
business continuity
1 -
climate change
1 -
cyber resilience
1 -
cyberresilience
1 -
cybersecurity
1 -
Edge and IoT
97 -
HPE GreenLake
1 -
resilience
1 -
security
1 -
Telco
108