Table 1 lists sources of process noise found useful thus far. Of these, turbulence is most common. Such sources probably are self-evident, except perhaps for burner flame instability, which indicates incorrect air/fuel ratio or impending flame-out in furnaces and boilers.
Trying to identify the source or sources of noise at the beginning of a smart pressure transmitter problem-sensing application project is helpful. However, rigorous analysis isn't worth the time and effort. For example, noise reflected off internal surfaces can alternately reinforce or cancel itself out, depending upon conditions, thereby confusing analyses. Also, the more viscous a fluid is, the more it absorbs sound, which makes transmitter or testing equipment placement a guessing game.
It's best to attempt to identify generally expected trends of what the signal source is doing at any one time versus what the process is doing then or might do later. Detecting an incipient problem or failure is the ultimate goal. Thinking ahead helps speed the development of a testable hypothesis that can be instrumented-up and trended during normal and abnormal operations. It's all quite empirical.
Three signal phenomena deserve particular attention.
Most obvious is a signal that gets stronger or weaker because of an abnormal situation. Impulse line plugging and distillation column flooding (discussed later) fall into this category. The faster the change occurs, the more positive the detection.
Second is where signal strength fluctuates more (or less) during abnormal conditions. Changes in mean and SD make those occurrences stand out graphically.
Less obvious is where the diagnostic signal is simply background process noise, such as from a pump or blower, and of no value of itself. Process-problem detection occurs when this signal, as it passes through a piece of process equipment, is attenuated or amplified during an abnormal situation. Noteworthy examples include: fouling in a heat exchanger and water absorption in a molecular sieve dryer (a swelling sieve changes the flow channel, which muffles the background noise).
In addition to trying to identify the source or sources of process-generated noise, an engineer should evaluate the physics of the noise through statistical manipulation to help develop a testable hypothesis.
For instance, in a fluid flow situation it's useful to know what the physics of the fluid are (its Reynolds Number (Re), viscosity, density, etc.), and their impact on noise creation, propagation and suppression — and therefore on transmitter positioning. The same can be said about the effects — good or bad — of orifice plates, venturis, valves and other substantial noise generators.
As a statistical example, a higher flow rate equals higher Re, equals higher turbulence, equals higher SD. Frequently, the SD increases and decreases quite linearly with flow rate. Dividing the SD by the mean (which gives the Cv), in effect, filters the SD by providing a Cv trend curve that stays relatively level compared to a rising and falling SD trend (Figure 2). Under abnormal conditions, the typically abrupt change in the Cv trend is often the most easily detected and alarmed.