Downtime is downright expensive
How many people know when their equipment is due for maintenance, upgrade or replacement? And how much confidence do they place in whatever tells them this? How about when assets are due to be replaced?
A survey by ServiceMax, the leader in field service technology, found that more than two-thirds (70%) of surveyed manufacturers don’t know when their equipment is due for maintenance, upgrade or replacement and 74% don’t know when assets are due to be replaced.
ServiceMax also found that unplanned downtime affected 82% of businesses over the past three years and cost an average $260,000 an hour. It’s reported that 42% of this downtime was caused by equipment failure at a cost of $50B annually (source IndustryWeek).
Making maintenance smarter to protect margins
Zero unplanned downtime is now a top priority for the majority of manufacturers. One way to make maintenance smarter is through artificial intelligence (AI). Artificial intelligence analyzes data collected from different sources and uses it to deliver actionable insight on the status of a machine component or the machine itself. AI has an unmatched ability to process large volumes of data, recognize patterns, make predictions, and give practical advice on actions to take.
According to digital transformation consultant Capgemini, machinery maintenance and quality are the leading AI transformation projects in manufacturing operations today. McKinsey says predictive maintenance can reduce machine downtime by up to 50% and increase machine life by up to 40%. Results like these are why global research and analyst firm, ABI Research expects AI-enabled industrial devices to have an installed base of 9.8 million by 2024.
With AI, planned maintenance can be based on actual usage and wear characteristics and maintenance can be very focused. Technicians are told by the machine which components need inspection, repair or replacement. These insights lead to a longer Remaining Useful Life (RUL) of machines, because secondary damage is avoided. AI can also help reduce auto-adjustment times, synchronize complex systems and enable autonomous decisions to be made.
Compact AI will bring BIG benefits to Manufacturers
Smart factory technologies like AI and predictive analytics are about to bring big changes to the factory floor thanks to Compact AI. Compact AI utilizes a deep-learning algorithm that recognizes only the information necessary for carrying out specific tasks and filters out irrelevant information. This makes it possible for AI to be embedded in a wide range of automation equipment including servos, inverters, programmable logic controllers, human machine interfaces, and more.
By embedding Compact AI into automation equipment, the equipment itself can monitor the quality of components, detect the smallest flaws in machinery, predict equipment failure, and prevent unplanned downtime. Increased use of Compact AI will lead to cost reductions in production processes and incremental improvements in operational efficiency while laying a foundation for networked, information-based automation systems.
Compact AI has already been designed into our servo system to lower maintenance costs and reduce machine total cost of ownership. When predictive maintenance capabilities are based within the servo itself, the servo amplifier can monitor belt drives, ball screws, linear guides and gears to detect and diagnose impending failures, improve uptime by detecting mechanical component deterioration through friction and changes in vibration values, and generate warnings in advance of failures.
Compact AI also enables quick tuning. Users can tune the servo mechanism in approximately 0.3 seconds. No tuning experience is required because gain values are automatically generated, reducing machine setup time and effort. The system is also able to effectively suppress vibration on both the load and the machine base at frequencies as low as 100 Hz.
Smart servos merge information technology (IT) and operational technology (OT), so they can support CC-Link IE TSN®, a time-sensitive network that enables time synchronization across all connected devices at 1 Gbps. This high-speed industrial Ethernet network also enables advanced edge computing benefits and easier IoT adoption.
AI also drives advanced diagnostics in our next-generation variable frequency drives (VFDs). Innovative use of Compact AI will make it possible to identify signs of inverter damage caused by hydrogen sulfide or other corrosive gases. When the production environment needs to be improved to avoid equipment failure and unplanned downtime, the operator will receive a notification. These new VFDs also have AI in the setup software to ease installation. The AI-based diagnostics can analyze downtime causes such as over-currents caused by acceleration bursts. Some inverters can detect deviation in load profiles, which may an indication of mechanical failure such as a clogged filter or a broken belt.
Built-in programmable logic controller (PLC) functions will allow inverters to communicate with each other to coordinate operation. Compact AI will even analyze and determine the lifetime of critical components, such as capacitors, contact relays, cooling fans and inrush current limit resistors.
AI-based Predictive Robot Maintenance
The future of manufacturing is dependent on higher levels of productivity through robotics and automation. The latest AI-based predictive maintenance simplifies robot adoption and enables plant and maintenance operators to easily understand, schedule and optimize robot maintenance.
This advanced functionality is delivered through a plug-in card that provides three maintenance functions:
- Consumption degree calculation, which determines when maintenance is required for drive parts such as ball screws, ball splines, gears, bearings and belts.
- Maintenance simulations, which aggregates consumption data, uses it to estimate the robot’s service life and offers a maintenance schedule based on real-world operating conditions. This function can be used even before installing the machine on the factory floor, which is especially useful for anyone concerned about the time and cost associated with industrial robot integration.
- Centralized robot management platform, which combines maintenance data with enterprise system data and utilizes cloud-based analytics to deliver highly reliable predictive models. Information generated from these predictive models can reduce the costs of both scheduled and unscheduled downtime.
Compact AI has many applications
While predictive maintenance is one of the top applications for Compact AI, there are many other use cases, including:
Quality control.In this case, Compact AI makes it possible to alert manufacturers when quality drops. Compact AI also makes it possible to collect and analyze data for improving quality.
Waste reduction.Compact AI can help you identify areas of loss and prescribe focused actions to reduce product defects and inefficiencies, so you can predict and prevent production waste.
Production optimization.Compact AI can search for production disturbances such as pressure deviations in pumps, leaking valves, inconsistent bearing temperatures, and more. Compact AI can then mine probable root causes and recommend the necessary improvements to remove the process or machine inefficiencies.
A Foundation for the Industrial Internet of Things (IIoT)
By upgrading your automation equipment today, you can increase accessibility and visibility of data collected on the factory floor and transfer information to other machines, networks or data management platforms. You can then use this data to drive improvements in overall equipment effectiveness (OEE), make decisions in real-time to influence the production process, and use data collected from PLCs, servos and drives to reduce maintenance costs, while optimizing product quality.
With intelligent, interconnected solutions, manufacturers can lay a foundation for Industry 4.0; a foundation that can be incrementally deployed with minimal investment. The potential payoffs of Industry 4.0 are enormous — more precise, higher quality manufacturing with lower operational costs, less downtime, and fewer injuries.
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