Companies incur huge losses when the different types of machinery they use fail without warning, causing downtime and time-consuming maintenance. Our client wanted us to build a predictive maintenance system that will prevent malfunctioning machines and improve the overall efficiency.
Even with periodical maintenance checks, agricultural machinery broke down affecting farming, growing and irrigational operations. The downtime was huge and disastrous.
Our client wanted a maintenance system that predicted beforehand the potential and underlying problems. It should be able to foresee failures, provide alerts and proactive responses. The data collected should be monitored in real-time and send alerts to smartphones.
A predictive maintenance system integrated with ThingSpine, connected devices and sensors that delivered real-time alerts and reports for immediate action and decision-making.
With sensors placed on the machinery and required locations, the data captured was constantly monitored. This is then transmitted and analysed in the cloud storage. When the alert or vibration shows an ‘error’, a high-level analysis was done in real-time and displayed through the IoT interface.
Reduced downtime by 20%
Reduced maintenance costs by 50%
Increased agricultural efficiency and productivity with climate, soil and watering sensing system.
Huge volumes of data are constantly monitored in real-time, enabling the administrator to take timely and informed decisions.
Data processing and analysis detects ‘issues’, identifies it and sends alerts to smartphones. This reduces delayed actions and decreases damages.
Machine-Learning algorithms are designed to optimize irrigation, water flow, pressure, cycles and timing.
All alerts and reports are delivered to mobile phones via the SMS, email and push notifications. All communications between the machines and the connected devices are through the cloud, eliminating the need for costly or complex infrastructure.
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