Fault Registration System


As explained on our sustainability page we are implementing SDG 9 for the manufacturing industry- the targets 9.2, 9.4 and 9B to be precise – so here is our flagship product for industry4.0. The world of textile manufacturing is marred with problems in the Quality Assurance departments the in-depth R&D of this company culminated in the strongest resolve to eradicate problems in QA for the textile industry. Industry 4.0 for Fabric QC ranks the Fault Registration System at the top of the precursors to proceed because of the never-before-imagined amalgamation of Internet of Things technology with Artificial Intelligence (AI).

The highly complex dynamics within the industry’s four four-point 0 ecosystem required Machine learning-based systems exclusively for the QC departments of fabric folding. The FRS (Fault Registration System) is the only premium ML/AI-powered RPA solution for both woven and non-woven fabric. It detects and identifies faults in Fabric during the Quality Control process and then marks them through a combination of industrial PCs, width measurement, and label applicator. Resolution of the biggest bottleneck for Textile manufacturers through the Internet of Things (IoT), with multiple types of fabric defects (tear, decolouring, etc.), saves $20,000/month for each textile unit.


The empowerment of the operator for digital recording of faults using a Fault Marking Keyboard and electronic meter to fetch meters leads to a 25% reduction in operational costs. The innovation is only halfway done till this part as there is an AI engine installed along with the system that analyzes how to cut the fabric to eliminate any kind of fabric wastage. The AI optimization algorithm is our flagship invention that produces a cutting plan optimization which is humanly impossible. All the data is presented smartly with bifurcations based on several filters that the users can apply according to impromptu requirements.

In-built multilingual support in the application interface enables operational efficiency with ease of use.

Smart Real-time decision making with fast navigation of data that is archived in organized sections.
Multiple sensors integration (weight, width, marking etc.) with machines for full capacity utilization of the system.

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