Predictive Maintenance Mobile App For Civil Engineering Machinery
Impala Intech created a predictive maintenance mobile app for civil engineering machinery on behalf of Local Government Engineering (LGED) Bangladesh
Client’s Goal - Improved Maintenance of Construction Equipment
LGED Bangladesh required a maintenance application that can accurately predict upcoming maintenance for civil engineering machinery parts
The Challenge for Impala
Business Challenges
The LGED was looking for ways to reduce/ eliminate machinery downtime due to lengthy manual inspections of different moving parts and replacements when they wear out
Technical Challenges
- Improving prediction accuracy
- Reducing measuring error
- Creating an updated solution from scratch
How Impala Intech Made it Possible
Authorization for Operators
The app allows different operators to join the database by signing up with an account, and they are given authority to operate certain machines based on their account user access
Dealers Map
The solution on both ends contain a map that will show the location of all dealers who can provide with replacement for malfunctioned equipment or their parts
Recent Diagnostics Info
With visualized databoards and graphical representation, the application offers recent diagnostics for all equipment currently in operation
Settings Management
The solution offers optimal setting presets for different equipment to increase their efficiency and reduce the risks of malfunction
Linking Several Excavators To A User Profile
A manager can use Product Identification Number (PIN) to link multiple excavators to a single profile for efficient equipment management
Our Tech Stack
Unity
Tensorflow
Mask-R-CNN
Object Segmentation
The Success of Our Solution
With the solution, LGED Bangladesh has successfully been able to reduce equipment malfunction and maintenance costs in multiple construction sites.
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