Innovations in PdM and RxM: Using new technology to maximize uptime and performance

Enhanced asset and service management

With AI and ML, maintenance timeliness and effectiveness are continually refined. The Coleman AI Platform from Infor operates below an application’s surface, mining data and using ML to improve the predictive and prescriptive maintenance process for organizations such as CERN (the European Organization for Nuclear Research), says Rick Rider, senior director of product management at Infor.

“With Coleman AI and Infor Enterprise Asset Management, CERN can predict air flow trends for individual devices, for example, and move from time-based preventative maintenance to predictive maintenance – subsequently reducing the number of false alarms and corrective interventions,” Rider observes.

Maximo Asset Monitor from IBM leverages AI to detect anomalies from incoming data that is sourced from existing operational technology (OT) systems. It enables non-technical operations engineers to apply AI models in a no-code fashion at enterprise scale to effectively monitor how operational assets are performing in near real time, says AJ Naddell, principal offering manager for Watson IoT at IBM.

AI-based condition monitoring “allows companies to move beyond simple threshold-bounded alerts that are difficult to manage and error-prone, as they are dependent upon whomever configured them,” explains Naddell. With Monitor, asset-intensive businesses can improve operational visibility and increase their return on assets (ROA), he adds.

Siemens and SAS partnered to deliver AI-embedded IIoT analytics for the edge and the cloud, which enhances remote monitoring, diagnostics, failure modeling, and predictive and prescriptive maintenance. Their combined solution provides access to streaming analytics technology from SAS within MindSphere, the cloud-based, open IoT platform from Siemens. It helps to cleanse, validate, and analyze burgeoning IIoT data streams from sensors and elsewhere, enabling continual awareness of the current state of events.

“Combining Siemens’ MindSphere and SAS analytics technology can help companies increase productivity and reduce operational risk through predictive/prescriptive maintenance and optimized asset performance management,” states Bill Boswell, vice president of cloud marketing at Siemens.

The predictive and prescriptive capabilities of Aquant’s Service Intelligence Platform power solutions such as ServiceMax Remote Triage. Aquant’s approach is to prescribe maintenance and service decisions by combining insights from historical service and maintenance data with institutional knowledge of subject matter experts.

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Aquant COO and Co-founder Assaf Melochna says the solution uses AI and ML to find “the best solutions to asset performance problems that are hidden in our clients’ structured data (like CRM databases and IoT alerts) and free text (like customer comments and field technician notes), and builds a prescriptive triage engine. Expert techs then validate and optimize the findings, helping every team member solve problems like experts.”

Targeted industry solutions

New industry solutions provide focused predictive and prescriptive maintenance opportunities. Honeywell Forge for Industrial, for Airlines, for Buildings, and for Inspection Rounds are examples. Josh Melin, product line director of Honeywell Forge for Airlines at Honeywell, says Honeywell Forge Connected Maintenance uses the latest AI, ML, and more conventional statistical models to find problems before they result in operational interruptions, and then alerts the airlines’ tech ops department so that they can act in time.

For airline customers, Melin observes: “Not only have we demonstrated that we can predict failures before they occur, but we also prescribe exactly what maintenance actions should be taken to avoid impact to the operation, reducing unscheduled maintenance events by 35%. These alerts plus additional dashboards and self-serve data exploration are all available on the Honeywell Forge for Airlines software solution.”

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ENSCO Rail’s Automated Maintenance Advisor (AMA) leverages autonomous track inspection capabilities and data analytics to automate identification of prescriptive maintenance tasks. It enables cloud-based condition deterioration trending of both linear and point assets, and produces timely maintenance recommendations for repairs and replacements. AMA can be integrated with enterprise asset management software to execute the approved tasks as work orders.

For automotive aftermarket service providers including manufacturers embracing servitization, Wipro Limited and Pegasystems together enable automated prescriptive maintenance of connected cars. Their solution combines Wipro’s Digital Prescriptive Maintenance application with Pega’s case management capabilities to ensure proactive vehicle maintenance and customer service.