Some process values do not sit comfortably on a normal chart. A vacuum pressure that ranges from atmospheric down to a tiny fraction of a millibar, or a conductivity that swings across several orders of magnitude, becomes unreadable on an ordinary linear scale, where the small values pile up against the bottom of the chart and disappear. A logarithmic trend axis fixes this by scaling the axis in decades rather than in equal steps, giving every order of magnitude the same amount of room. This guide explains how a log axis works, how it contrasts with a linear axis, and when it clarifies an operator's reading versus when it can mislead.
Logarithmic trend axis in one line: A logarithmic trend axis scales the vertical axis so that each equal distance represents a tenfold change - a decade - rather than a fixed number of units. This keeps values spanning many orders of magnitude readable at once, because small and large values both get proportional room, whereas a linear axis crushes the small values against zero and shows only the large ones clearly.
On a linear axis, equal distances mean equal differences: the gap from ten to twenty is the same height on the chart as the gap from ninety to one hundred. That is the intuitive behavior most trends use, and for values that stay within a modest range it is exactly right. The trouble comes when a value ranges across orders of magnitude, because a linear axis sized to show the largest values gives the smallest ones almost no room. A pressure that swings from a thousand down to one tenth is drawn with the low end squashed into a sliver at the bottom, where changes that matter are invisible.
A logarithmic axis changes what equal distance means. Instead of equal differences, equal distances represent equal ratios: the gap from one to ten is the same height as the gap from ten to a hundred and the same as a hundred to a thousand, each step being one decade. This gives every order of magnitude identical space on the chart, so a value at the low end has just as much room to show its behavior as a value at the high end. A trend that was an unreadable line hugging the bottom on a linear scale becomes a clear, legible curve on a log scale.
The two are not competing versions of the same picture; they emphasize different things. A linear axis makes absolute differences visible and is the honest choice when the operator cares about how many units a value changed. A log axis makes proportional changes visible and is the honest choice when the operator cares about how many times a value changed. Choosing between them is really choosing which of those two questions the trend is meant to answer.
A log axis is at its best for values that genuinely live across many decades. Vacuum pressures are the classic case: a pumpdown might start near atmospheric and finish orders of magnitude lower, and only a log axis can show the whole descent with the low-pressure region readable. Conductivity, certain analytical measurements, and pressure-decay tests behave the same way, spanning ranges where the interesting behavior often happens at the small end that a linear scale would hide. In these cases a log axis is not a stylistic choice but the only way to see the data.
A log axis also has a neat property for rates of change. On a log scale, a process that decays or grows by a constant percentage per unit time - exponential behavior - appears as a straight line, and its slope is the rate. This makes a log trend a natural tool for reading decay behavior: a leaking vessel whose pressure falls exponentially plots as a straight line, and a deviation from that straight line signals something changing. Operators who work with decay or first-order behavior often prefer a log axis precisely because it turns a curved decay into a slope they can read.
The danger is using a log axis where it misleads. Because a log scale compresses the high end and expands the low end, it visually shrinks large absolute changes and magnifies small ones near the bottom, which can give a false impression of stability up top and drama down low. For a value that stays within one order of magnitude, a log axis adds distortion for no benefit and should not be used. And because the spacing is non-uniform, an operator reading a log trend by eye must remember that equal heights are equal ratios, not equal amounts - a habit that takes care, and a reason log axes are reserved for the cases that truly need them rather than applied by default.
In everyday SCADA the great majority of trends are linear, because most process values - a flow, a level, a tank temperature - stay within a comfortable range where linear scaling reads naturally. The logarithmic axis is a specialist tool kept for the measurements that need it, and part of building a good trend is recognizing which of the two a given tag calls for rather than defaulting to one everywhere. A conductivity or vacuum trend that would be useless on a linear scale becomes clear on a log one, while a routine level trend is only made harder to read by log scaling.
For remote monitoring, the log axis matters most for the specialized measurements found on certain sites - analytical instruments, tightness and pressure-decay tests, and any process where the meaningful action happens across a wide range. Being able to review such a trend on a log axis from a central location means an engineer can interpret a wide-range measurement at a distant site as readily as one standing in front of the panel, rather than being defeated by a trace that a linear scale would have flattened.
On a cloud SCADA platform such as Merobix, the underlying data is stored at full resolution regardless of how it is displayed, so an operator can review the same recorded values on either a linear or a logarithmic axis and choose whichever answers the question at hand. That flexibility is useful because the right scaling depends on what is being asked: an engineer might view a pressure-decay record on a log axis to read its decay slope as a straight line, then switch the same data to a linear axis to read the absolute pressure lost. Because the choice of axis is only a way of viewing the stored history rather than a property of the data itself, the operator keeps both readings available from the same record.
Use a log axis when the value ranges across several orders of magnitude and the interesting behavior happens at the small end - vacuum pressures, conductivity, and pressure-decay tests are common examples. A linear axis would crush those small values against zero and hide them. For values that stay within roughly one order of magnitude, a linear axis is clearer and a log axis only adds distortion.
A logarithmic axis makes equal distances represent equal ratios, so a value that changes by a constant percentage per unit time plots as a straight line, with the slope equal to the rate. This is why operators use log trends to read decay behavior: a vessel whose pressure falls exponentially appears as a straight line, and any departure from that line signals a change in the process.
Yes. A log axis compresses the high end and expands the low end, so it visually shrinks large absolute changes and magnifies small ones near the bottom, which can suggest false stability up top or false drama down low. It should be reserved for values that genuinely span many decades; for narrow-range values it distorts the reading without adding any benefit.
Merobix reads your field devices into a cloud SCADA - the real thing behind these terms, live in days from any browser.