Let Mac's Auto Repair in Cherry Valley keep your GMC running reliably by performing regular maintenance and repairs. For many fleet owners their GMC trucks are important and reliable assets that allow them to get tough jobs done every day.
The GMC brand has become synonymous with engines that offer uncompromising, dependable power and torque. GMC is an offering from General Motors that blends performance and reliability into an affordable package. Repair and maintenance for all GMC make and models including:Īt Mac's Auto Repair in Cherry Valley we diagnose and repair GMC cars, trucks, and SUVs with precision. Predictive maintenance can be used across industries, including in the oil and gas and manufacturing sectors, to identify faulty equipment and save lives, time and money down the line.įorbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives.50 | 490 Main St Cherry Valley, MA 01611 GMC Auto Repair and Maintenance Services in Cherry Valley Call Mac's Auto Repair for GMC Repair Services However, the AI use case doesn’t stop with electric poles. government every year at the hands of wildfires. With the power of AI, utility companies can help reduce the billions of dollars that are spent by the U.S. Wildfires are a debilitating problem in California and elsewhere across the world, yet they are one that technology can help prevent. The team responsible for maintenance should receive a structured report that allows for easy consumption of the insights and appropriate action to be taken. Once the AI detects the anomalies, it will then assess whether or not action needs to be taken and make these recommendations to the utility company. Step 5: Report anomalies to the maintenance organization The aim of this technology is to soon be able to analyze tens of thousands of images per day. This process is called object recognition. The data science team must train the AI model with labeled and annotated images so it can recognize all potential objects that could feature in the images - including trees, fences or leaves - and learn from any wrongly identified items. In addition, the tags on the electric poles should always be featured and readable in the relevant images.ĭetecting potential anomalies (or defects) in the images involves training the neural network so that the next time the model identifies the same object in an image, it can determine not just that it is an anomaly but what kind of anomaly (debris obstruction, damage to the pole, etc.). Image quality can be determined using a machine learning algorithm that divides the image into pixels and compares the color change from one pixel to another.
Here, teams can use mathematical models to understand the number of images taken from each angle and identify the ones which are low-quality. The total number of images collected for a given pole must cover its entire surface area so that the AI has enough coverage to identify a defect in any of its parts. This step is about ensuring that the algorithm has access to enough high-quality images to be able to produce accurate results. Step 3: Validate that the AI has everything it needs This step also highlights the importance of data engineering practices within data science projects - it’s not only about the AI models but also about how the data being fed to them is treated and stored beforehand. Otherwise, data quality and access may be compromised. Teams should consider who is the owner of this data, and who is responsible for safeguarding it. There needs to be proper data governance at this stage.
Once the images are collected and stored, the rest can be automated with AI. The format of the images should be usable and consumable. They could be stored in the cloud or any file system. A structured and organized approach here will reduce complexity when using and analyzing the images down the line. Once taken and verified for quality, the images must be stored in a reliable, well-performing, accessible and organized storage system. Step 2: Store the images in a secure, accessible location It’s also vital to employ quality assurance at this stage to ensure that the pictures have been taken correctly (i.e., from all angles and without blurriness or obstruction in the images). The team conducting the analysis can contract third parties to take these photos with drones or helicopters. Utility companies must set up an accurate automated process for image collection, which will provide the key dataset for analyzing the condition of the electric poles. Step 1: Set up an automated process of image collection of the poles Let’s break down the steps required to automate this process with AI.
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