Industry consolidation with worldwide competition is putting today's equipments and maintenance strategies under intense financial pressure, which makes operations and maintenance budgets to be among the first to be cut. Greater productivity and economic benefits are possible when operators have the desired tools and real-time information to continuously optimize economic factors for the loops they control, as well as to reduce costs. Many of the factors that affect industrial plant economics change frequently from raw material costs to market demand for part and equipment outputs. In an ideal world, operators would constantly select energy and feedstock sources, product mix, equipment used, and other variables to optimize the economic performance of the plant. In the real world, however, operators may not always have the real-time feedback data on the economic effect of their actions. At this point, plant users and operators are unaware that they're losing millions of dollars by running the plant at sub-optimal operating points. Even with the available information at their disposal, they may not have the relevant tools needed to evaluate complex interactions between variables, or to determine the best operating points before conditions change again.
Predictive maintenance, abnormal situation prevention, optimum selection, early detection of failures, reliable troubleshooting, economic optimization, and monitoring strategies offer clear productivity and cost benefits. But predicting potential problems and the effect of changing conditions requires a constant flow of real-time information, not just about the process, but also about the myriad pieces of processes, parts and equipments that make it work. That is something traditional automation architectures can't easily provide. The control system can't show you much more than the process variable and any associated trends and alarms. There is no way to monitor real-time equipment health or reliability and thus no way to detect the early-warning signals of potential problems.
In order to prevent problems before they occur, many industries have come to rely on preventive maintenance, through minimizing unexpected downtime by performing inspections, parts replacement, and other maintenance activities, at predetermined intervals. Clearly, the downside to preventive maintenance is cost, even for a problem as common as changing an engine's oil, there is no definitive evidence, or consensus on the best time to perform maintenance.
Knowing the health and reliability status of an operating machine, equipment, part and process reaches beyond the functions of traditional reliability centered maintenance, and affects a part's or equipment's entire life, which involves the integration of design, manufacturing, operation and maintenance events. This is the question that drives the author to providing a sustainable solution through integrated reliability monitoring, maintenance and repairs of industrial parts and equipments with Technological Inheritance Model-based Software Program.
Technological Inheritance technique is the transference of the optimum quality characteristics of parts from an initial part surface finish operation to the final life cycle operation of equipments. The technique is used to eliminate negative traits and failures, while maximizing the positive quality characteristics of parts, processes and equipments.
With the use of Technological Inheritance Model-based software, there is a far greater chance that the product will be introduced to the market on time, be accepted, operate reliably, maintained cost effectively and be profitable.
The best way therefore to select life cycle components in operation and maintenance projects is the use of technological inheritance model-based software program. As the operation and maintenance project progresses from the initial stage to final stage technological inheritance model-based software can therefore be used to select, validate and verify project productivity towards reliability, functionality, viability and sustainability.