A luggage carousel is composed of different motors, wheels and carbon components.
These components can be affected by overweight or misplaced luggage.
The above usage causes the motors to undergo abnormal friction and therefore temperature rise.
Overweight and misplaced luggage on the carousel can result in flexing and cause the motor ‘s sliding motion to perform abnormally and ultimately can cause halt of the whole carousel.
When the carousel is in halt or malfunctioning state then it can cause delays to flights due to passengers and connecting flights waiting for luggage and passengers can even lose baggage on arrival to their destinations. So these delays can ultimately result in loss of millions of dollars to airport flight operations.
One carousel can have different components/machines which work in conjunction to achieve transport over the belt. These include motors, slats, speed generators. In order to get data from these components we have used different sensors that send their data to AWS IoT Cloud as below
0 [No light, Healthy Slat], 1 [Light found, Broken Slat]
Acceptable [5–10] non acceptable [>10 or <5]
Low [<5] Normal[5–10] High [>10]
For this project, I have used sensors from IFM and Cloud gateway from CloudRail.
With a combination of AWS IoT Services and the above hardware providers, I have designed and implemented my architecture as below.
This architecture explains following steps
Installation of Vibration, Light, Temperature, Tachometer and Tensile Sensors
Connection of sensors with I/O link module and Cloud Gateway
CloudRail Device Management Cloud for definition of sensors and its connection to AWS IoT
Unstructured IoT Data flow from CloudRail DMC to AWS IoT Core and through AWS IoT Core ‘s rule based engine shifted to AWS IoT Analytics for Data processing through pipe line where unstructured data from IoT sensors is filtered, transformed and enriched.
The enriched data is utilized in SPICE Datasets of AWS Quick Sight for graphical output. The graphical output is used by factory users for different insights.
The raw data is utilized in AWS IoT SiteWise for display. AWS IoT SiteWise already has asset modelling defined for sensors and Cloudrail can easily integrate directly with AWS IoT SiteWise. End users can see this data directly through the AWS SiteWise monitor dashboard.
Enriched data is utilized by trained dataset in AWS Sagemaker (trained using Linear Regression Algorithm on machine sensor data) and based on Machine learning model evaluation we can invoke AWS API gateway which can procure specific machine maintenance parts and services.
I am able to output the following insights using AWS QuickSight.
Vibration variations when the carousel is overloaded or in idle mode e.g. In this graph we can see that the NorthLine15 carousel is having more vibrations on sampled 1st rounds.
Motor temperature variance in crowded carousel operations. e.g. in this graph we can see SouthLine5 is getting increased temperatures in a couple of rounds.
Slat fault detection with Light Sensors. Here we are detecting slat difference on straight and curved paths and here we can see Northline15 is having slat bending or misplacement or tearing detected in a couple of rounds.
After the initiation of an automatic order by machine learning algorithm in the procurement system, the following notification is sent to the procurement manager for approval.