V2M, a pioneer of a novel method that leverages sound to detect and diagnose vehicle malfunctions, is thrilled to announce game-changing technological advancements. Through the application of a cascading neural network system, this innovative method promises to reshape the industry by delivering faster and more precise diagnostic outcomes. At the same time, V2M has achieved a remarkable feat by reducing the sensor count from three to just two, introducing a fresh approach. The last version of software opens up the realm of predictive diagnostics. This means a solution, not just addressing current issues; it is foreseeing and preventing potential ones.
The power of fewer sensors
One of V2M’s remarkable achievements lies in minimizing the number of sensors needed for automotive diagnostics, reducing the requirement from three to two. This simplification streamlines the implementation process by making the design lighter to save valuable time and resources, ultimately enhancing the efficiency and accessibility of this crucial service. By reducing the number of sensors, we streamline the design, making implementation easier and, naturally, lowering the cost.
“Reducing the number of sensors from three to two is of paramount importance because it significantly lowers the cost and complexity of implementation. We embarked on this journey with six acoustic sensors, and the prospect of implementation seemed like a distant dream. Today, with only two sensors on our hardware side, we are on track to surpass even the boldest expectations and achieve a final system cost within the $100 range, said Petr Bakulov, V2M CEO.
Neural networks take the stage
At the heart of V2M’s innovative diagnostic methodology is the utilization of a cascade of neural networks, each specialized in detecting specific faults. This approach enables swift and pinpoint identification of precise vehicle issues. Each neural network is trained to identify its distinct fault type, ensuring the utmost accuracy in results.
Supercharged audio data analysis
V2M has also significantly enhanced its audio data processing capabilities, enabling more in-depth analysis of sound signals. This enhancement empowers the system to detect even faint, sharp, or uncommon sounds that could indicate vehicle faults. But what truly sets the technology apart is its ability to unearth faults even before they’re evident to humans. Sound appears where it hasn’t been heard before by the human ear.
The system operates at a level where it perceives sounds before users do. V2M is not merely flagging the existence of a fault; it is embarking on an investigative journey, tracing the trend that leads to a malfunction. In essence, a company is at the roots of predictive diagnostics by identifying the trajectory of a fault before it fully manifests.
What lies ahead
While V2M has made significant strides in automotive diagnostics, the company remains committed to further expanding the list of detectable faults and reducing the diagnostic time for brand-new vehicles. This ongoing effort is geared toward enhancing service quality and ensuring road safety. Here’s where it gets even more exciting: the V2M system isn’t confined to a specific set of sounds.
In fact, it can be trained with any sound. Placing the system in a room amidst a cacophony of extraneous sounds, the invention immediately detected typing on a keyboard, showcasing its remarkable capability. This means that you can employ the system for tracking and security applications. Moreover, it’s not just limited to automotive diagnostics; the universal approach can also revolutionize fault studies in factories where machines emit distinct sounds before wear and tear. This showcases the adaptability and universality of the latest developments of V2M.
V2M COMPANY is a New York-based entity leading the charge in AI-powered vehicle diagnostics. Drawing upon decades of experience in vehicle operation, we have harnessed our own patented technology to create a pioneering solution for identifying car issues based on the distinctive sounds of real malfunctions. Our system is engineered to recognize car malfunctions by the sounds a vehicle makes. It’s nothing short of the future – here, today.
Photo credit: The feature image is symbolic and has been done by Christopher Isak with Midjourney for TechAcute.