The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section.
Power plants, waste water treatment plants, factories, airplanes, and automobiles are some examples of complex systems that include multiple machines operating to accomplish objectives. These complex systems include physical components that degrade over time, components that fail, and components that are being used incorrectly or sub-optimally. Degradation, failure, or incorrect or sub-optimal use of a given component in the system may affect other components of the system that depend on the given component.
As a component operates in the system, the component may be configured to operate differently during different operating states. For example, a machine may power up, warm up, run, cool down, and shut down. The machine may be configured to produce little or no output during the power up state; whereas, the machine may be configured to produce maximum output during the run state. Regardless of how a component is configured, the component can behave unexpectedly in any operating state. In a complex system, multiple components may behave unexpectedly for a long period of time even though the system as a whole may operate relatively efficiently over that period of time.
Operation of various components in the system may be monitored using sensors, which measure and report data about the operational behavior of the components. The sensors themselves are also susceptible to degradation, failure, and sub-optimal use, and this susceptibility creates uncertainty around the measurements themselves and around the behavior of the components in the system. The sensors may feed into customized triggers that provide alerts when measurements go outside certain boundaries. The customized triggers may be set up by engineers, such as maintenance engineers, that are assigned to oversee operation and maintenance of the machines and to promote overall health and efficiency of the system.
Accordingly, the overall health and efficiency of the system may be highly dependent on the knowledge, skill, expertise, and accuracy of the maintenance engineer, who is a human being. The overall health and efficiency of the system may also depend on a variable degree of uncertainty surrounding the sensors and the behavior of the components in the systems. In light of the complexity of the system, there are often few human beings who are able to make the accurate judgments required by the maintenance engineer, and even fewer who are available to verify the correctness of the judgments made by the maintenance engineer. Although the output of a given system may be observed at a high level, there is usually little or no knowledge of how much better the maintenance engineer could be performing.
Further, some machines come with manuals or specifications that explain, to the maintenance engineer, how frequently to perform certain maintenance operations on the machines. Due to the wide variety of systems and changing operating environments in which the machines may be used, such manuals or specifications often grossly over-estimate or under-estimate the frequency in which such maintenance should be performed in a given environment. Such problems are often difficult to detect and often lead to inefficiencies that exponentially increase as the size and complexity of the system increases.