The dynamic industrial world requires swift action and decision making, particularly when equipment failures arise. Consistent assessment of vital assets, essential infrastructure, and operations can help detect, alert, and address operational irregularities. Both condition monitoring and predictive maintenance play crucial roles in achieving this objective. By analyzing and interpreting the status of various types of motorized moving machinery, businesses can minimize unplanned downtime, enhance asset lifecycle management and maintenance deployment, and ultimately, reduce safety risks.
Waites: Making the World Run Smoothly with Condition Monitoring
Waites, a prominent figure in the field of condition monitoring since 2006, offers plant and facility managers a comprehensive view of their operations through 24/7, plug-and-play online monitoring. Their wireless sensor system is designed to revolutionize predictive and preventive maintenance, featuring a range of sensors and gateways for monitoring, and analyzing tri-axial vibration and temperature data. By implementing Waites in their facility, plant and facility managers can have their equipment monitoring system operational in less than 5 minutes.
Machine Learning Revolutionizing Factory Floors
AI-powered systems unlock timely interventions by training a model, detecting anomalies, and continuously monitoring equipment parameters. AI/ML uncovers hidden insights into sensor data, turning condition monitoring into a proactive, cost-effective, and data-driven process. Waites employs artificial neural networks, including deep neural networks, convolutional neural networks, and transformers, along with large language models (LLMs) to discern user interactions. They also utilize anomaly detection and forecasting techniques to monitor various internal and telemetry metrics, categorizing equipment defects in high-dimensional multivariate time-series data.
Design Waites future-proof sensors that are resilient, scalable to various facility sizes (with thousands of different machinery), and capable of transmitting over 100 KB of data even in the most challenging RF conditions. These sensors must efficiently operate in low-power modes to extend battery life and reduce the frequency of battery replacement. Furthermore, ensure that the sensor provisioning process takes less than 1 minute to complete.
Tailored for mesh IoT wireless connectivity, Waites chose the ultra-low power, multi-protocol MG24 Silicon Labs SoC, with an integrated AI/ML hardware accelerator. With its exceptional RF receiver sensitivity, an output power up to 20 dBm, and extensive Flash and RAM memory, the MG24 SoC guarantees top-notch, low-latency wireless connectivity, ideal for the data-intensive, long-range, battery-operated sensors.
By embedding the Silicon Labs MG24 SoC, Waites expedites the deployment of their sensors, enabling swift availability to customers in diverse industries. Thanks to the Silicon Labs pre-programming CPMS service, Waites can streamline device setup and installation, reducing the provisioning process to just 45 seconds.
Silicon Labs MG24: Powering the Waites Data-Intensive AI Sensors
Industries like manufacturing, logistics, and healthcare are increasingly embracing digital infrastructure asset management. Waites is at the forefront, deploying their sensors in multi-story facilities housing over 100,000 critical assets, even in challenging RF environments. These sensors are designed to ensure precise, uninterrupted real-time data processing. Fast and efficient device setup and installation are a crucial advantage when deploying condition monitoring sensors, with provisioning taking less than a minute for a competitive edge.Waites chose Silicon Labs EFR32MG24 multiprotocol SoC to ensure reliable, robust, and secure wireless communication for its data-intensive, battery-powered sensors. Targeted towards mesh IoT wireless connectivity, the MG24 SoC presents exceptional RF receiver sensitivity, and output power up to 20 dBm for extended range. Time critical I/O functions can be offloaded from the CPU using the Peripheral Reflex System (PRS), and 1.5 MB of Flash and 256 kB of RAM provide a solid memory footprint.With its low power consumption profile, MG24 ensures a battery lifetime span of up to 5 years for the Waites deployed sensors. Furthermore, thanks to the Silicon Labs pre-programming CPMS service, Waites can streamline device setup and installation, reducing the provisioning process to just 45 seconds and enabling swift availability to their customers.
Utilizing the MG24 built-in AI/ML matrix vector processor (MVP) hardware accelerator, Waites can complete sizeable complex mathematical operations of time-series data up to 8x faster while consuming 8x less energy.
In collaboration with Waites, Silicon Labs delivers a comprehensive solution comprising top-devices, established software, and the PSA Level 3-Certified Secure Vault, the most advanced level of security certification obtainable.