10 research outputs found
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Statistical techniques for automating the detection of anomalous performance in rotating machinery
Surveillance techniques which extend the sophistication existing in automated systems monitoring in industrial rotating equipment are described. The monitoring system automatically established limiting criteria during an initial learning period of a few days; and subsequently, while monitoring the test rotor during an extended period of normal operation, experienced a false alarm rate of 0.5%. At the same time, the monitoring system successfully detected all fault types that introduced into the test setup. Tests on real equipment are needed to provide final verification of the monitoring techniques. There are areas that would profit from additional investigation in the laboratory environment. A comparison of the relative value of alternate descriptors under given fault conditions would be worthwhile. This should be pursued in conjunction with extending the set of fault types available, e.g., lecaring problems. Other tests should examine the effects of using fewer (more coarse) intervals to define the lumped operational states. finally, techniques to diagnose the most probable fault should be developed by drawing upon the extensive data automatically logged by the monitoring system
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Statistical techniques for automating the detection of anomalous performance in rotating machinery
The level of technology utilized in automated systems that monitor industrial rotating equipment and the potential of alternative surveillance methods are assessed. It is concluded that changes in surveillance methodology would upgrade ongoing programs and yet still be practical for implementation. An improved anomaly recognition methodology is formulated and implemented on a minicomputer system. The effectiveness of the monitoring system was evaluated in laboratory tests on a small rotor assembly, using vibrational signals from both displacement probes and accelerometers. Time and frequency domain descriptors are selected to compose an overall signature that characterizes the monitored equipment. Limits for normal operation of the rotor assembly are established automatically during an initial learning period. Thereafter, anomaly detection is accomplished by applying an approximate statistical test to each signature descriptor. As demonstrated over months of testing, this monitoring system is capable of detecting anomalous conditions while exhibiting a false alarm rate below 0.5%
Pollution swapping in arable agricultural systems.
Pollution swapping occurs when a mitigation option introduced to reduce one pollutant results in an increase in a different pollutant. Although the concept of pollution swapping is widely understood, it has received little attention in research and policy design. This study investigated diffuse pollution mitigation options applied in combinable crop systems. They are cover crops, residue management, no-tillage, riparian buffer zones, contour grass strips, and constructed wetlands. A wide range of water and atmospheric pollutants were considered, including nitrogen, phosphorus, carbon, and sulfur. It is clear from this investigation that there is no single mitigation option that will reduce all pollutants