In a recent survey of information technology (IT) and operations executives from sectors such as manufacturing, utilities, power and energy, and transportation, 80% of respondents affirmed that the industrial internet of things (IIoT) can and will transform their companies and markets.
Integrating existing plant IT with “intelligent” and connected physical assets is the foundation of the Industry 4.0 movement. Increased automation to reduce operational risks and control costs is one reason IIoT makes sense for manufacturers and wind plant operators.
The IIoT or industrial internet of things may be the biggest driver of operational productivity and growth in the foreseeable future. Management consulting firm Accenture estimates that IIoT could add $14.2 trillion to the global economy by 2030.
However, a shift to “edge” (or asset) computing introduces a higher level of performance and production capabilities. Edge computing facilitates data processing at or near the source of data generation. For a wind-farm operator, this may look like a remote-control center connecting with and retrieving meaningful data from individual wind turbines on health and performance.
“Sensor and connectivity costs have come down significantly in recent years,” shares John Crupi, VP of IoT Analytics at Greenwave Systems, an IIoT service and software company. “Additionally, computing power at the edge has increased while its costs have also dropped. This has led to major opportunities for edge-based analytics software to detect and respond to issues faster — greatly benefiting manufacturers and operators such as wind owners.”
With conventional IIoT, sensors collected and transferred data in batches to a data control center or the cloud for processing, but this model is changing. With edge computing, “smart” equipment performs analytics on most of the data collected in real-time or in collaboration with nearby gateways. Only the product of edge analytics — so small volumes of pertinent or high-value data — is relayed to the cloud for storage or further analysis. This greatly reducing costs and the magnitude of data transmitted and stored.
Greenwave’s AXON Predict detects potential failures days in advance, which alerts organizations to perform preventative maintenance on an asset before a breakdown in service even occurs. By integrating edge analytics into wind-farm systems, operators can monitor issues in real-time to reduce generation disruptions, overall costs and optimize field-service operations.
For wind operators, the new IIoT model means obtaining specific and critical operational data faster and more reliability.
“Maintaining the operational efficiency of turbines is critical to wind farms because small component malfunctions may lead to significant problems that affect generational productivity and energy transmission,” says Crupi. “Equipment malfunctions are time-intensive and costly to fix, but the IIoT can help deliver valuable insights into potential faults or failures in wind turbines.”
Predictive edge analytics forecast whether a machine is likely to function optimally, falter, or fail based on accumulated asset operation metrics, the particular asset’s historical state, and incoming real-time data. That type of digital “clairvoyance” translates into cost savings.
“Since it comes down to cost and performance, wind operators should expect condition monitoring to provide key health and performance insights, including the capability to detect anomalies that cause performance issues and unscheduled downtime,” he says.
Here are three tips for choosing an effective IIoT-based software system.
1. Review and compare the latest trends in edge-based monitoring systems. These systems are grounded in lower cost hardware, standards-based cellular communication, and openness for extensibility and growth.
2. Treat the edge (at the wind turbine itself) as a new intelligent tier. New generation edge analytics systems push much of the processing to the asset for real-time detection and action while using the cloud as an aggregator and coordinator.
3. Incorporate data scientists and wind techs into the response. Use the edge as a dynamic system, which “learns” the behaviors of wind turbines and supports the ability to “inference” AI models built on the cloud and deployed to the edge.
“Today’s wind-farm operators can benefit from IIoT-connected software with advanced analytics, which essentially monitors and learns turbine behavior,” says Crupi. “Such systems can also make adjustments for enhanced asset performance analytics and greater data insights.”
A word of warning, however: “Security — or the lack of it — is a major risk. Many industrial systems were designed without considerations of connectivity,” he says. “Therefore, they must rely on extremely secure, perimeter-based systems to protect assets from intrusions and attacks, which provide a high level of analytics.”
Filed Under: News, Software
