Internet of Things is intelligence personiﬁed. Connecting devices at your home or oﬃce seamlessly- IoT is also proﬁcient at connecting devices that may not be classically associated with information technology. This creates a treasure trove of data and in the instance of inventory management, enables the creation of smart warehouses.
A smarter, productive and accurate way to manage warehouses utilises the scalability and dependability as oﬀered by the Internet of Things technology. With IoT, every item processed is tracked and recorded on inventory management systems throughout the supply chain procedure. Products can be easily located by ID numbers irrespective of where they are, and if they are inbounding as part of a shipment or are on their way to a customer’s doorstep.
There are many advantages to creating a smart warehouse with the latest supply chain technology. Devices, sensors and radio-frequency identiﬁcation (RFID) tags can enable warehouse managers to know the exact location and progress of any product at any time. “Hands-free” wearables can allow workers to move about and access information and instructions from anywhere in the warehouse without being constrained by workstations. Additionally, investing in IoT can reduce the use of manual labor, increasing speed and shipping accuracy, and oﬀer retailers an opportunity to obtain unparalleled visibility into inventory and supply chains. Many companies are already experimenting with IoT to create smart warehouses. UPS is using smart glasses in test programs to reduce the amount of labeling on packages. Athletic sportswear company Lids is using an Internet of Everything (IoE)-based robotics system to make its warehouse more eﬃcient, with robotic carts that pick products, place them in bins and deliver them to workers. On a larger scale, many companies such as Amazon are now even using autonomous robots in their warehouses.
Many manufacturing, warehouse and distribution facilities focus on condition monitoring or predictive maintenance solutions for the larger motors, drives and gear boxes but fail to take into consideration mission-critical equipment like smaller motors, bearings, rollers, conveyors, water pumps, etc. By and large, the maintenance for these happens on an inefficient, planned schedule at best, or only after the damage is done, at worst. An unplanned motor failure can cause a line-down situation ranging anywhere from a couple of hours to several days, resulting in downtime. While unplanned maintenance is more disruptive and expensive than routine scheduled maintenance, the best condition monitoring solution is one which predicts failure or presents the deterioration rate for any equipment that has the potential to cause downtime, in time to react. Bluvision’s Advanced Condition Monitoring uses machine learning and Artificial Intelligence (A.I.) to predict failure on any motor or mechanical equipment, weeks or months before the failure happens.
The sensors in BEEKs Industrial BLE beacon, collects historical vibration data and establishes a motion fingerprint of each individual motor or mechanical device. These fingerprints are collected over a short period of time where the monitored asset is operated normally and is run through all possible stages of operation. (Eg: Motor off/motor at low speed/motor at high speed, etc.)
Bluvision’s Condition Monitoring is based on multiple events and not just when a single anomaly is detected. While evaluating the new RMS and peak-to-peak values against the training stage, policies and alerts can be created for:
Bluvision’s solution, apart from being equipment agnostic, requires minimal hardware – sensor beacons to mount on the equipment and BluFi – WiFi gateways. Each gateway can manage hundreds of sensor beacons concurrently. Our Bluzone cloud allows for user-defined alerts and for fleet management so users can check status and health of thousands of beacons at the same time.
Our Condition Monitoring solution studies the motion in all 3-axis – x, y and z. More precisely, we use RMS (Root Mean Square), peak-to-peak, which provides the entire range of motion. The machine learning calculations are performed within the individual beacons with only peak-to-peak data (Low speed @ 10 Hz and high speed @ 800 Hz) transmitted to the cloud, thereby saving battery and ensuring the user doesn’t have to go through tons of unnecessary data to analyze and detect anomaly.
Most importantly Bluvision’s equipment agnostic solution is designed to understand the motion and predict the failure of any mechanical device providing factories and enterprises the ability to optimize operational and process flow with avoidable downtime.