Prescient, a leading provider of cloud-to-edge data automation software and solutions, announces the release of its low-code predictive maintenance solution kit. Developed jointly with National Control Devices (NCD), a top provider of long-range industrial wireless sensors, and WAGO, a leader in interconnect, interface, and automation solutions, the new Prescient Predictive Kit is the most customizable and extensible predictive maintenance solution on the market.
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Predictive maintenance is one of the most popular Industry 4.0 applications. Using sensor data such as vibration, current, and temperature to predict machine failures often weeks in advance, predictive maintenance systems save significant cost due to unplanned downtime. According to Statista, the market will grow from US$4.5 Billion in 2020 to US$63.3 Billion in 2030, exhibiting a CAGR of 30% during the forecasted period.
Today’s predictive maintenance solutions are either turnkey solutions that have limited flexibility or custom-built solutions that require significant technology development. Prescient, NCD, and WAGO are releasing the industry’s first low-code predictive maintenance solution kit. This solution enables customers to customize its predictive maintenance solution without requiring software development expertise.
The Prescient Predictive Kit includes an NCD wireless predictive maintenance sensor, a WAGO edge computer, and a low-code predictive maintenance template dataflow inside Prescient’s flagship data automation software, Prescient Designer.
The NCD wireless predictive maintenance sensor is one of the most advanced on the market. It includes a vibration sensor with a frequency range between 1.56Hz to 6.4kHz and a sample rate up to 25.6kHz. It also includes an AC current sensor with a range up to 100A RMS, and a high-grade k type thermocouple temperature sensor with a rating of 260 degree-C. The NCD wireless predictive maintenance sensor has an encrypted communication range of up to 2 miles.
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Anil Bhaskar, NCD’s CEO, says, “Our predictive maintenance sensor is specifically built to predict the failure of rotary machines based on vibration, current, and temperature data. It is one of the most advanced predictive maintenance sensors on the market. Its industrial performance and long-range wireless capability mean that it can monitor machines operating in the most difficult environments.”
The Linux-based WAGO Edge Computer features an Intel® Atom E3845 Quadcore 1.91 GHz processor, 8GB of RAM, and supports a variety of physical interfaces. In addition, the Prescient Predictive Kit is compatible with WAGO’s full line of edge controllers and computers, ranging from small, compact controllers to powerful Intel i7-based edge computers, giving customers full flexibility in choosing the edge computing power they need.
“Modern control systems require highly capable computers and that’s where the WAGO 752 Edge computer family fits,” commented Jesse Cox, WAGO’s Senior Application Engineer. “We can pair these with low-code technology innovations like Prescient Designer and Prescient Edge runtime, and hardware leaders like NCD sensors to bring the most advanced functionality to your control system’s edge-of-network.”
The Prescient Predictive Kit dataflow is a low-code solution. It allows customers to collect NCD wireless sensor data, quickly create alerts, and display sensor data and alerts on a local or cloud-based dashboard. Customization capability is critical, as each customer may have different requirements.
“At Prescient, we’ve been working on predictive maintenance projects for many years,” said Ashish Yadav, Prescient’s Director of Software Development. “We have seen many different requirements so we’re happy be able to bring the benefits of this experience to the marketplace.”
The Prescient low-code solution allows customers to quickly visualize sensor data and customize the detection algorithm any way they want. For example, they could analyze vibration data in the time-domain, or analyze vibration data in the frequency domain, or feed the data into a machine-learning model.
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