Kuj ne voli istoriju... :: Nauka ~ Tehnika ~ Military

Mrzi me da porevodim... eve vi tekst:

People today expect that they can quickly check their investment portfolios over the last quarter, year or multiple years with a few mouse clicks. When you visit Amazon.com, your recent purchase and viewing history is available. This typifies our growing appetite for historical information in our personal world. Yet, many processes lack the same basic historization and analysis tools that we find beneficial in our personal lives. If data historization is desirable and adds value to your 401(k) investments, why wouldn’t you demand it for your process data?

Although the idea of data historization is straightforward and historians already have demonstrated their value across industry, the road to successful implementation can be treacherous. One of the early speed bumps that you’ll encounter is justification of implementation and maintenance costs. How can you assess the value of data to which you don’t currently have access? Predicting or quantifying in advance the discoveries of patterns and events from these data may not be possible but experience has shown that they almost always occur. Sometimes these discoveries can be dramatic, resulting in significant savings that far outweigh the cost required to implement data historization. Analysis of past “mistakes” may help with early detection of current process issues, allowing you to prevent lost or scrapped batches and material. The projected value of the solution should include expected savings due to increased production, reduced waste and less downtime. Take advantage of demo or trial versions of historization software you’re considering — this may lead to early recognition of data patterns and events before the full solution has been implemented.

Value in History
The primary reason to implement a data history solution is to gain a deeper understanding of your data so you can reduce waste, improve efficiency and save money.

In batch processes, manufacturers strive to replicate the perfect or “golden” batch. With historical batch data and the proper analysis tools, you can determine what contributes to the golden batch. Initially, you may be able to pinpoint batches that were highly successful, although you may not know what lead to each success. Analysis of successful batches versus unsuccessful ones can result in identification of key process measurement profiles; successful batches may share a common profile for one or more related process measurements. As new batches begin, tracking these measurements against historically created profiles can boost the percentage of successful batches.

A data historian and the right tools and resources, coupled with continuous data collection during uptime and downtime, allows analysis that can provide insights about production downtimes, enabling you to increase your runtime, product output and profits. A few third-party software products target this task; they leverage existing historians or implement their own data historization specifically for downtime analysis.

Historical data also offer benefits for diagnostics and predictive maintenance of equipment such as pumps and valves. They can allow you to follow the degradation of a part over time so that preventative maintenance can occur when needed. They can prevent unexpected failures due to broken parts, premature wearing or other unexpected mechanical problems. For example, you can track the torque level of a valve actuator over time to see variances from the norm.

Sometimes, regulatory or industry requirements mandate implementation of a data historian. In such cases, their strict stipulations may narrow your solution choices.

Regulatory requirements for your process, particularly for industries such as pharmaceuticals, may demand that you provide data history as well as may mandate that you perform extensive certification of your data historization and presentation tools — thoroughly testing and documenting accuracy of data retrieval, storage and playback. This may lock you into current software versions because making any changes to the system, including upgrading software to the latest version, may require that you recertify upgraded components. Due to these strict requirements, some companies implement “uncertified” installations that can be frequently upgraded and modified, with the understanding that data contained in the system are also uncertified and therefore can’t be used when making process or production decisions.

Requirements for continuous emissions monitoring systems (CEMS) have steadily expanded to more geographical areas and industries. CEMS involves collection, storage and reporting of data related to emissions such as NOx, SOx and CO2. In some of the stricter reporting areas, data must be electronically reported daily to the supervising district. In many cases there’s oversight and validation of the implemented solution, requirements for long-term and accurate storage of emissions data, rules and requirements for calculating and reporting data, and heavy financial fines for incomplete, missing or inaccurate reporting.

Eve vi Link:

http://www.chemicalprocessing.com/articles/2009/091.html