Amazon Lookout for Vision provides a machine learning (ML)-based anomaly detection service to identify normal images (i.e., images of objects without defects) vs anomalous images (i.e., images of objects with defects), types of anomalies (e.g., missing piece), and the location of these anomalies. Therefore, Lookout for Vision is popular among customers that look for automated […]

One of the challenges encountered by teams using Amazon Lookout for Metrics is quickly and efficiently connecting it to data visualization. The anomalies are presented individually on the Lookout for Metrics console, each with their own graph, making it difficult to view the set as a whole. An automated, integrated solution is needed for deeper analysis. In this post, we use a Lookout for Metrics live detector built following the Getting Started section from the AWS Samples, Amazon Lookout for Metrics GitHub repo. After the detector is active and anomalies are generated from the dataset, we connect Lookout for Metrics to Amazon QuickSight.

Predictive maintenance can be an effective way to prevent industrial machinery failures and expensive downtime by proactively monitoring the condition of your equipment, so you can be alerted to any anomalies before equipment failures occur. Installing sensors and the necessary infrastructure for data connectivity, storage, analytics, and alerting are the foundational elements for enabling predictive maintenance solutions. However, even after installing the ad hoc infrastructure, many companies use basic data analytics and simple modeling approaches that are often ineffective at detecting issues early enough to avoid downtime. Also, implementing a machine learning (ML) solution for your equipment can be difficult and time-consuming. With Amazon Lookout for Equipment, you can automatically analyze sensor data for your industrial equipment to detect abnormal machine behavior--with no ML experience required.

This is the second post in the two-part series on how Tyson Foods Inc., is using computer vision applications at the edge to automate industrial processes inside their meat processing plants. In Part 1, we discussed an inventory counting application at packaging lines built with Amazon SageMaker and AWS Panorama . In this post, we discuss a vision-based anomaly detection solution at the edge for predictive maintenance of industrial equipment. Operational excellence is a key priority at Tyson Foods. Predictive maintenance is an essential asset for achieving this objective by continuously improving overall equipment effectiveness (OEE).

AWS works with Pfizer to support more rapid innovation and improved clinical manufacturing operations to help develop tomorrow's therapies Inc. Company, announced that it is working with Pfizer to create innovative, cloud-based solutions with the potential to improve how new medicines are developed, manufactured, and distributed for testing in clinical trials. The companies are exploring these advances through their newly created Pfizer Amazon Collaboration Team (PACT) initiative, which applies AWS capabilities in analytics, machine learning, compute, storage, security, and cloud data warehousing to Pfizer laboratory, clinical manufacturing, and clinical supply chain efforts. For instance, AWS is helping Pfizer enhance its continuous clinical manufacturing processes by incorporating predictive maintenance capabilities built with AWS machine learning services like Amazon Lookout for Equipment (AWS's service for detecting abnormal equipment behavior by analyzing sensor data). As a result, Pfizer can maximize uptime for equipment such as centrifuges, agitators, pulverizers, coaters, and air handlers used in clinical drug manufacturing.

Discrete and continuous manufacturing lines generate a high volume of products at low latency, ranging from milliseconds to a few seconds. To identify defects at the same throughput of production, camera streams of images must be processed at low latency. Additionally, factories may have low network bandwidth or intermittent cloud connectivity. In such scenarios, you may need to run the defect detection system on your on-premises compute infrastructure, and upload the processed results for further development and monitoring purposes to the AWS Cloud. This hybrid approach with both local edge hardware and the cloud can address the low latency requirements and help reduce storage and network transfer costs to the cloud.

Organizations of all sizes and across all industries gather and analyze metrics or key performance indicators (KPIs) to help their businesses run effectively and efficiently. Operational metrics are used to evaluate performance, compare results, and track relevant data to improve business outcomes. For example, you can use operational metrics to determine application performance (the average time it takes to render a page for an end user) or application availability (the duration of time the application was operational). One challenge that most organizations face today is detecting anomalies in operational metrics, which are key in ensuring continuity of IT system operations. Traditional rule-based methods are manual and look for data that falls outside of numerical ranges that have been arbitrarily defined.

With machine learning (ML), more powerful technologies have become available that can automate the task of detecting visual anomalies in a product. However, implementing such ML solutions is time-consuming and expensive because it involves managing and setting up complex infrastructure and having the right ML skills. Furthermore, ML applications need human oversight to ensure accuracy with anomaly detection, help provide continuous improvements, and retrain models with updated predictions. However, you're often forced to choose between an ML-only or human-only system. Companies are looking for the best of both worlds, integrating ML systems into your workflow while keeping a human eye on the results to achieve higher precision.

In this post, we look at how we can automate the detection of anomalies in a manufactured product using Amazon Lookout for Vision. Using Amazon Lookout for Vision, you can notify operators in real time when defects are detected, provide dashboards for monitoring the workload, and get visual insights from the process for business users. Amazon Lookout for Vision is a machine learning (ML) service that spots defects and anomalies in visual representations using computer vision (CV). With Amazon Lookout for Vision, manufacturing companies can increase quality and reduce operational costs by quickly identifying differences in images of objects at scale. Defect and anomaly detection during manufacturing processes is a vital step to ensure the quality of the products. The timely detection of faults or defects and taking appropriate actions is important to reduce operational and quality-related costs. According to Aberdeen's research, "Many organizations will have true quality-related costs as high as 15 to 20 percent of sales revenue, in extreme cases some going as high as 40 percent." Manual inspection, either in-line or end-of-line, is a time-consuming and expensive task.

Google launched Visual Inspection AI, a new service to identify production defects in manufacturing units. The service uses the state-of-the-art computer vision models developed by the AI research teams at Google. Vertex AI AutoML Vision, an integral part of the managed AI platform, delivers similar capabilities. Customers can upload images and classify them based on labels before initiating a training job. AutoML Vision generates a fully-trained model hosted in the cloud or deployed at the edge for performing inference. Visual Inspection AI takes AutoML Vision to the next level through its domain knowledge of the manufacturing industry.