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Every hour of unplanned machine downtime costs manufacturers up to $260,000 in lost production. Even a short outage can delay deliveries, reduce customer satisfaction, and eventually negatively impact the bottom line. Companies are constantly seeking solutions to avoid ineffective maintenance routines to stay competitive, which is driving a growing demand for replacing the traditional scheduled maintenance practices with IoT predictive maintenance.
What is Predictive Maintenance?
Predictive maintenance involves using various connected sensors to monitor the condition of production equipment. In other words, it helps manufacturers predict when a machine is likely to break down and the optimal maintenance time before any failures occur.
Benefits of Predictive Maintenance
By being proactive about equipment fixes, manufacturers can save millions of dollars and improve operating efficiencies. Predictive maintenance enables companies to predict equipment health and extend lifetime while increasing production yield and keeping employees safe.
How Predictive Maintenance Works
Condition monitoring sensors capture and collect critical equipment insight for a 360-degree view. All data points such as temperature, vibration, sound, torque, current, voltage, and magnetic field information are sent to the network edge or cloud for computing. The monitoring system generates data thresholds and sends alerts when anomalies are identified.
This can be done either by integrating the wireless connectivity within the machinery or by retrofitting existing equipment with sensing infrastructure.
The Silicon Labs Approach to IIoT Predictive Maintenance
Silicon Labs empowers IoT device makers to engineer reliable wireless predictive maintenance solutions for their industrial customers with a portfolio of wireless SoCs and modules that feature best-in-class RF performance and power consumption. By infusing Machine Learning (ML), Silicon Labs enables complex motion detection, sound recognition, and image classification on memory-constrained and remote edge devices.
Designing Industrial Predictive Maintenance Devices
The following is a rundown of key design considerations for developing wireless devices that will reliably function in high-interference industrial sites.
Increasing RF Transmission Power
Industrial settings present many obstructions to RF propagation, including electrical noise, metal structures, and rotating equipment. Transmission power becomes a critical consideration at the very early design stages. With the world’s highest TX power of 20 dBm and ultimate receiver sensitivity, Silicon Labs wireless hardware such as EFR32BG12 Bluetooth SoCs and BGM210P Bluetooth modules enable you to develop reliable IIoT wireless devices.
Avoid False Positive Alerts
Any network latency can lead to false positive alerts, triggering unnecessary maintenance requests. False positives detract from the monitoring system’s credibility and lead to revenue loss by ineffective maintenance resource allocation. With reliable network connectivity via high transmit power hardware and robust software stacks, Silicon Labs ensures data is safely transmitted from the connected sensors to the central data store.
Maximize Battery Life for IIoT Devices
Most predictive maintenance wireless solutions are battery powered and deployed in hard-to-reach locations. This presents new challenges for developers. By using a battery-friendly wireless standard like Bluetooth, smart solutions can operate using minimal power with an extended range. This, coupled with the ultra-low power wireless chip set from Silicon Labs creates a solution that maximizes battery life.
How to Enable Machine Learning (ML) in Predictive Maintenance
Infusing ML on the chip allows your IIoT solutions to learn from the industrial environment. Based on the collected data points, ML will detect anomalies based on operational patterns and predict the appropriate maintenance timing. You can implement ML at different levels, yet by computing the data locally, you’ll be ensuring faster decision making and energy savings.
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