Microphones and structure borne sensors provide the operator with not just the ability to listen to the components off site, it also monitors the wind turbine 24/7 using anomaly detection.
Discover our case studies and success stories
Welcome to Neuron Soundware’s Case Studies Hub, where you can explore our case studies and success stories. Read inspiring narratives showcasing how our AI-driven sound analysis technology is revolutionizing industries. Discover real-world transformations and the remarkable power of sound analysis.
In the past operators tested the quality of the window regulators via listening. This meant the testing approach was dependent on the human factor and listening by ear. The manufacturer decided to replace this manual and demanding method by automatic system delivered by Neuron Soundware.
A leading manufacturer of automotive subassemblies required a rapid end-of-line automatic test bench to ensure the quality of the entire product.
Many industrial manufacturing heavyweights acknowledge that Neuron Soundware excels in developing AI and ML algorithms to optimize industrial manufacturing equipment and processes through analysis of sound and other physical parameters.
Wind energy has become an increasingly vital component of the global push towards sustainable and renewable energy sources. As the number of wind turbines continues to grow, ensuring their efficient and uninterrupted operation is of paramount importance. One key aspect of this is the implementation of predictive maintenance techniques, which can help identify potential issues before they lead to costly downtime. Neuron Soundware (NSW) has developed a method for predicting and preventing mechanical failures in wind turbines based on the use of sound emission analysis and other physical parameters of wind turbines.
To ensure uninterrupted car window production, a European automotive supplier employed NSW technology to monitor critical equipment—oil-injected rotary screw compressors—using IoT devices and non-intrusive sensors. AI and Machine Learning assessed acoustic data in real-time to provide early alerts for potential failures, allowing prioritized inspections and cost reduction. Read the expanded case study here.
A Neuron Soundware solution was deployed for detecting faults in pneumatic door components of trains, as a result of high penalties for broken train doors which prompted the exploration of preventive measures. Read the expanded case study here.
In this case study, NSW's AI and Machine Learning solution tackles wear in CNC machine tools. By employing Deep Learning and regression, the solution offers real-time, in-situ alerts for tool quality deviations, enhancing efficiency, cutting costs, and boosting productivity. Experience amplified profitability through zero scraps, reduced waste, and optimized operations.
The customer required a solution to address the detrimental impact of cavitations in pump equipment on machinery and manufacturing quality. Utilizing NSW technology, anomalous cavitation sound signals were detected promptly, enabling swift elimination of unforeseen failures. NSW technology effectively resolved a problem conventional monitoring systems can't address.
Efficient port operations rely on the health of mechanical components in material handling equipment. This solution employed certified NSW IoT devices to gather acoustic and vibration data which, together with AI and Machine Learning analysis, provided actionable insights, enabling remote 24/7 monitoring, curbing unplanned downtime, cutting maintenance expenses, and enhancing equipment lifespan, safety, and reliability.
In this application, Neuron Soundware addresses industrial production challenges using mobile communication (4G and 5G) to enhance equipment performance. By acquiring and processing acoustic data from operating robots, their AI technology identifies unusual robotic arm movements. This solution ensures robust, cost-effective production by preventing damage and anomalies that could compromise the integrity of the production process, with multiple accompanying benefits.
An IoT sensor-equipped nGuard solution monitors both the gas engine itself and the generator. Alerts are sent to the plant operators to either turn the engine off or schedule maintenance – based on suggestions from our diagnosticians.
Neuron soundware delivered a solution for monitoring wear of the turbine bearings. 24/7 monitoring and nGuard platform offer more flexibility than standard solutions on the market.
High-speed drawing of copper coated material risks damage to a critical die, leading to costly production wastage. NSW's innovative solution ensures real-time monitoring, preventing deviations and enhancing efficiency.
A customer faced challenges in achieving consistent granular specifications during 18-hour milling cycles, resulting in quality issues and wastage. The NSW AI and Machine Learning solution was implemented to monitor the milling process, optimizing particle size and reducing production time by 2 hours per batch. This led to an 11% efficiency boost, reduced wastage, and enhanced supplier credibility.
Deployed to prevent the breakdown of components in specialized grinding machines, the NSW AI and Machine Learning solution monitors the process, preventing damage and maintaining correct viscosity of the material on the plant. Benefits include lower maintenance costs, fewer unplanned stoppages, reduced production expenses, increased productivity, and less material wastage.
Addressing the challenge of maintaining steady air supply in fluctuating manufacturing demand, the NSW solution employs AI-driven monitoring and adjustments. By analyzing real-time data, it ensures optimal air delivery, offers early warnings, and prioritizes inspections, effectively preventing disruptions and reducing operational costs.
In the context of a European automotive manufacturer, the use of a piston helium compressor in transmission gear hardening is vital for production. This study addresses the challenge of maintaining uninterrupted operations in the tempering furnace, as equipment failure could lead to production delays and scrap generation. Previously, entire compressors were replaced due to critical incidents. The proposed solution employs IoT devices and non-intrusive sensors to gather and analyze acoustic data. AI and machine learning algorithms assess compressor sounds, promptly detecting deviations from the norm as anomalies. Benefits encompass early detection of potential failures, real-time asset monitoring, preemptive alerts, streamlined inspection prioritization, and reduced costs tied to failures and scrap.
This case describes a solution for overstrained escalator mechanical units which provided real-time uptime reports and remote access. The solution entails producing utilization reports, enabling efficient maintenance scheduling, and providing essential operational insights. Benefit from cost savings through remote monitoring, benchmarking of operation and maintenance effectiveness, and access to vital machine data.
A collaboration with NeuronSW involves utilizing IoT for tracking acoustic emissions from bearings, mechanical seals, and reactor shells. NeuronSW manages hardware installation, data acquisition, ML algorithm training, and service deployment. The project aims to enhance reactor agitator monitoring, benefiting from NeuronSW’s expertise in acoustics, software, and hardware development.
In pursuit of production continuity, a leading wheel rim manufacturer sought a solution to monitor vital air compressors using IIoT devices and AI-driven anomaly detection. By analyzing sound data, this end-to-end system provides real-time insights, enabling early intervention, asset prioritization, and cost reduction while optimizing workforce efficiency.
Addressing additive manufacturing challenges, the NSW solution offers real-time monitoring of 3D printers using acoustic signatures, images, and physical data. This innovation detects material faults during printing, curbing wastage, enhancing quality, and boosting efficiency. Benefits include material savings, reduced post-production testing, heightened productivity, profitability, and supplier reputation.