Predictive maintenance is a maintenance strategy that uses sensor-based industrial data to monitor the condition of assets and detect anomalies, based on machine learning methods and algorithms, in order to perform maintenance in a proactive manner.

Predictive quality is the ability to determine product quality and anomalous process conditions from machine learning and process data in order to infer quality before issues occur, prevent product defects and recalls, reduce scrap, and achieve greater process transparency.

Predictive Maintenance

Traditional preventive maintenance is reactive and arises after an asset failure has occurred, which can cost companies thousands or even millions of dollars. By using an analytics-driven approach, companies can stop issues before they transpire, improving uptime, asset performance, and company ROI.

Predictive Quality

Traditional quality strategies include regular inspection, which involves more expense and manpower. With a predictive approach, companies can determine process deviations and product issues before they occur in order to reduce unnecessary physical inspections and product defects.

Predictive Maintenance

When deploying a predictive maintenance solution, it’s important to prioritize critical assets in order to have the greatest impact on production bottlenecks, downtime costs, and overall ROI. Criticality analysis is the systemic method of evaluating risks and ranking assets. This approach includes considering the age of the equipment, the frequency of failures, the duration and cost and/or downtime associated with the failures, and the cost of the asset. Criticality analysis is typically done on an overall asset level first. Once the critical assets are determined, analysis is performed at a component level to better understand which components are the most critical within the asset. These components are then monitored using a predictive maintenance solution.

Predictive Quality

Predictive quality solutions leverage process data or quality information, such as images, to pinpoint issues before they affect final product quality or to identify bad parts before they reach the end of a production process. This helps increase production, reduce scrap and rework, and minimize warranty claims and recalls. Predictive quality has the biggest impact in high volume production applications or where quality inspection is costly, inefficient, or ineffective.

Predictive Maintenance

Predictive maintenance solutions provide companies with an early indication of emerging failure trends and patterns. These solutions deliver an understanding of critical asset health through data insights by highlighting whether conditions are anomalous and isolating where the problem has occurred, as well as other additional pertinent health information.

Predictive Quality

Predictive quality solutions showcase what variable or part of the process has contributed to the anomalous process condition. Analysis outputs predict these process issues before they occur, improving not only the quality of future parts, but the overall production.

Predictive Maintenance

Predictive maintenance gives companies the knowledge needed to efficiently schedule maintenance before failure occurs, reducing unplanned downtime. A proactive maintenance policy helps to improve productivity, advance asset performance transparency, and minimize spare part inventory, increasing profitability.

Predictive Quality

Predictive quality solutions help identify product quality problems and undesirable process conditions before they occur. By catching bad parts early in the process and detecting quality issues, companies can prevent line shutdowns and reduce scrap.

Predictive Maintenance

Predictive maintenance solutions can be used in a wide set of industries and applications, including manufacturing, semiconductors, robots, aerospace, automotive and transportation vessels, freight and cargo, oil and gas, water treatment, utilities, and power generation.

Predictive Quality

Predictive quality solutions are applied in a more narrowly focused set of industries, including automotive manufacturing, aerospace manufacturing, electronics manufacturing, discrete manufacturing, and process manufacturing, such as chemical, glass, steel, and aluminum manufacturing.