A range of capabilities to meet current and future needs:
AI algorithms for real-time acoustic recognition
IBM Watson IoT Acoustic Insights leverages deep learning algorithms to identify sound patterns from unstructured acoustic data. These models can be trained in the cloud to identify anomalies, defects and product quality issues both in finished products and products operating in the field.
Defect models integrate cognitive and human expertise
The solution employs a combination of machine learning based upon curated acoustic recordings of defects and human knowledge to create a library of defects that can be easily deployed to automate quality inspections and diagnostic analysis across the manufacturing and post-sale processes. Via machine learning and human expertise the library can easily be extended to build inspection solutions for new or variations of existing products.
Provides non-destructive means for inspection
The use of sound pattern recognition helps firms non-destructively identify, monitor and proactively remedy quality problems. Clients will also be able to inspect products free of environmental obstructions that can make tests unobservable.
Distinguishes multiple defects through single sensor
Using this technology quality inspectors or repair technicians can distinguish multiple types of faults based on sound. For example, a vehicle engine test can distinguish multiple types of failures based on subtle difference in sound profiles.
Start fast with continual defect model improvement
Increase the accuracy of the model and improve over time based on “training” from human based inspectors and SMEs. Over time the classification of defect types are automatically brought back to the training model.
Eliminates knowledge silos and shortens inspection training
Acoustic-based manual inspection requires highly trained and skilled personnel. For many, the required years of experience and knowledge tends to be siloed. IBM Acoustic Insights augments the knowledge across inspectors and decreases variability. This is important as many firms grapple with high labor turnover and inexperienced new hires.
Early detection of equipment degradation
Deploy on manufacturing equipment to detect subtle signs of wear well before damage becomes an expensive problem. Acoustic patterns can be used to identify root-cause analysis and suggested maintenance.
Predicts future equipment failure
Integrate with other IBM analytical tools and methods to detect potential equipment failures based on audio wear patterns.