• Asset performance monitoring information provides critical information about the actual state of an organization’s assets and infrastructure. Using manufacturer-provided information about designed performance windows may be a reasonable starting point, but the real-world performance of an asset needs to be taken into account. Asset performance monitoring can yield important data and insights that help reduce downtime and increase reliability.
• Ultimately, the data collected about assets and facilities need to be analyzed in order to be of any value to an organization. Some insights that may be too subtle to be deduced by humans can be detected by machine learning algorithms. As machine learning and artificial intelligence software increase in power and ability, the value of asset performance monitoring will grow.
• Asset performance monitoring specifically concerns the assets currently controlled by the organization or enterprise. Although it may provide important insight during the acquisition of assets, asset performance monitoring does not include the purchase or sale of an asset.

Sensors: automated and manual

• Modern machinery and equipment are packed with sensors that can provide critical information for asset performance monitoring. This information can be uploaded to a database within the enterprise’s infrastructure, or stored in the cloud. The continuous flow of information provided during the operation of the asset is a crucial piece of asset performance monitoring.
• Certain types of data can only be collected during manufacturing downtime. For instance, visual inspection of a seal or gasket may require an asset to be partially disassembled, which requires planned downtime, during which the inspection may take place. Even inspection and monitoring processes that don’t explicitly require production to be halted may necessitate equipment that is prohibitively expensive to leave in place, and thus may not be able to provide continuous data during operation.

Portals, visualizations, and analysis

• Asset management software should include a way for plant operators to access the data collected from assets. Software-as-a-service solutions, in which the asset management software is hosted in the cloud, may provide web portals for both upload and download of asset information. This allows personnel to be able to quickly access information critical to asset performance in the field. Using a mobile platform, an employee at a remote facility or asset can get the same bird’s-eye view they would get sitting in front of a screen at headquarters. This real-time feedback allows for nimbler asset management software.
• With sensorization providing an ever-expanding volume of data for reliability managers to sift through, concise and illuminating visualization and analysis tools are increasingly important. In time-critical situations, years of valve pressure information presented as a row of numbers in a spreadsheet may be worse than useless. It can be easy to lose time trying to find meaning in poorly presented data; this can result in missing important clues to production shutdowns.
• A well-presented visual representation of asset performance monitoring data can inform operators on asset maintenance requirements, but better yet are newly developed machine learning analysis tools. Machine learning algorithms can find signals that indicate developing maintenance demands and alert operators with enough lead time to allow for informed and economically prudent asset maintenance choices. Known as predictive maintenance and prescriptive maintenance, these tools can even help schedule asset maintenance according to operator-provided priorities.