Preventing Downtime With Predictive Analytics

predictive maintenance

Infosys and the Industrial Internet Consortium are testing how predictive analytics could help reduce downtime for aircraft landing gears.

While asset efficiency is not yet on manufacturers’ radar screens, it should be. Monitoring and addressing asset efficiency enables enterprises to mitigate or prevent downtime in a range of systems and devices, from factory floor production equipment to aircraft parts. By doing so, companies can save significant amounts of money, as well as enhance safety.

[ Related: 7 IIC Testbeds Offer a Look at the Future of the Industrial IoT ]

A recent study conducted by Infosys and the Institute for Industrial Management (FIR) at Aachen University found that 85 percent of global manufacturing companies are aware of asset efficiency, but only 15 percent have managed to implement it. Current challenges include lack of instrumentation for assets, missing real- time data analytics, missing information from other systems, and failure to include aspects of efficiency such as energy, utilization, operations, and serviceability.

A Testbed for Predictive Maintenance

To help understand and address these requirements, the Industrial Internet Consortium (IIC) recently approved Infosys Asset Efficiency Testbed. The effort is being led by Infosys, along with support from Bosch, GE, IBM, Intel, and PTC.

The testbed is targeted at high tech, industrial manufacturing, discrete and process manufacturing, automotive, aerospace, and other segments with high value fixed or moving assets. “Ok, I realize ‘asset efficiency’ might sound like something a mutual fund salesman would sell you,” says Dr. Richard Soley, executive director of the IIC. “However, in this case, it is an exciting technology opportunity that promises to save significant downtime, dollars, and even lives.”

Asset efficiency solutions analyze real-time and historical data across key health parameters and predict the serviceable life of assets. The main goal is to collect asset information more efficiently and accurately, in real-time, and also enable usage of analytics to help companies make the right decisions.

The IIC testbed will focus on aircraft landing gear, the performance of which can be enhanced. Predictive maintenance, enabled through asset efficiency solutions, will extend the life of these subsystems. The reference architecture of this testbed is built on Infosys Information Platform (IIP) and leverages sensors, application enablement, device management capability, and edge devices.

Using predictive analytics, the Asset Efficiency Testbed is designed to collect real-time asset information efficiently and accurately and run analytics to make the right decisions in terms of operations, maintenance, and asset replacement. The Asset Efficiency Testbed is a vertical testbed, making it possible for the testbed to be applied to multiple solutions.

The testbed will launch in two phases. In the first phase, the testbed will be created for a moving solution, in this case, aircraft landing gear. The focus of this phase will be on the creation of the stack and the integration of technologies. In the second phase, the testbed will address fixed assets, such as chillers, with the goals of finalizing the architecture and opening up the interfaces.

Features of the testbed include condition monitoring, which measures and tracks key health parameters of the assets and ensures they stay within allowed ranges. The testbed also enables diagnostic analysis, in which an analytics engine analyzes data, compares it with past and related data in context, and identifies anomalies. A third feature consists of prognostics, in which algorithms predict remaining useful life for the assets or its components.

The Asset Efficiency Testbed uses “a holistic approach incorporating operational, energy, maintenance, service and informational considerations,” Soley says. The testbed offers benefits including: condition monitoring to help determine an optimal maintenance schedule; reduction in downtime to improve overall productivity of assets; reduction in capital and operational expenditures; and efficient energy use.

IIC also oversees a number of other testbeds geared to specific functions. Those include:

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About Joe McKendrick

Joe McKendrick is RTInsights Industry Editor and industry analyst focusing on artificial intelligence, digital, cloud and Big Data topics. His work also appears in Forbes an Harvard Business Review. Over the last three years, he served as co-chair for the AI Summit in New York, as well as on the organizing committee for IEEE's International Conferences on Edge Computing. (full bio). Follow him on Twitter @joemckendrick.

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