Vibration Sensor Machine Diagnostics 2026: Measurable vs Unmeasurable Failures

When people ask us about vibration sensors for machine diagnostics, the complaints split into two opposite camps. One is “we installed the sensors, and the alert never fired before the line went down.” The other is “the alerts fire so often that nobody looks at them anymore.” Both complaints trace back to the same root cause, and it is not sensor accuracy and not the intelligence of the analytics engine. It is that the mounting points were decided purely on how critical each machine is, without first separating out whether the way that machine actually fails shows up in vibration at all.

Which failures are you actually seeing with vibration sensor machine diagnostics?

A vibration monitoring discussion almost always starts with the question “how many machines do we put sensors on?” Someone opens the critical equipment list, sorts it by how painful a stoppage would be, and draws a line where the budget runs out. As a procedure it feels natural. But that sorting exercise contains no physics. Nowhere in it does anyone ask whether vibration is the right physical quantity for the failure modes in question. A machine can sit at the very top of the criticality list and still fail in ways that a vibration sensor will never detect.

So swap the starting point. Instead of “which machines are critical,” begin with “how did this plant actually stop over the past year?” Once you know how many of those stoppages were of a kind that vibration can see, you have almost everything you need for the investment decision. The denominator of the business case comes from the failure history, not from the asset register.

Vibration only picks up four mechanical modes on rotating machines

What a vibration sensor is genuinely good at is mechanical deterioration in rotating machinery. In practical terms it comes down to four modes.

• Unbalance – the mass distribution of the rotating element is uneven. The vibration appears at the same frequency as the shaft speed.
• Misalignment – the shaft centrelines of the driver and the driven machine do not line up. It shows up as vibration around the coupling.
• Looseness – foundation bolts, base plates, bearing housings and similar fixings have worked loose.
• Bearing damage – spalling or wear on the outer race, inner race, rolling elements or cage of a rolling element bearing.

Turn that list around and the implication is blunt: every other way your line stops is invisible to a vibration sensor, no matter how expensive the hardware. A relay failure inside a control panel. An inverter trip. A broken wire. A proximity sensor giving a false reading. A workpiece or a piece of scrap jamming. A chipped die or fixture. A changeover done in the wrong sequence. These are either electrical and informational events, or mechanical and human ones that never present as vibration on the surface of a bearing housing. Nothing guarantees that measuring vibration on the rotating parts will catch any of them.

When someone says “we fitted vibration sensors and the machine still went down,” the story is usually not about sensor performance at all. The line stopped because of a failure mode that does not appear in vibration. If that distinction is never made, the next move tends to be “let’s add AI on top,” and the organisation ends up spending more money trying to see something that was never in the signal to begin with.

Count the breakdown of your stoppages first (5 out of 8 in this article’s model plant)

To make the rest of this article concrete, we will work with a model plant: a Japanese-owned factory in Thailand, 180 employees, with 15 rotating machines (pumps, blowers, gear-driven conveyors and air handling units) as candidates for vibration monitoring. Every figure and every count that follows is a calculated estimate under these model conditions. None of it is measured performance data from a real site.

In this model plant, the 15 candidate machines produce 8 unplanned stoppages per year. Split by failure mode, they break down like this.

Then go one step further. Of the 5 events that do appear in vibration, we assume that 4 can realistically be caught in advance if the mounting points and thresholds are appropriate. The remaining 1 progresses too quickly: the machine fails completely between two interval measurements, so the monitoring system never gets a chance to warn anyone.

That narrowing from 8 to 5 to 4 is the single most important sequence of numbers in this article. The numerator of your return calculation is 4, not 8. As long as you are thinking in terms of 15 machines or 30 measurement points, that number never appears anywhere in the conversation.

The good news is that counting the breakdown does not require any system at all. Pull the downtime records, the daily shift reports and the repair tickets for the last 12 months, lay them out, and classify each event one by one into “mechanical deterioration of a rotating machine” or “something else.” If the records do not exist, or if the cause column just says “adjusted” or “repair complete,” then fixing that is the more urgent piece of work. We have covered how to raise the granularity of stoppage records in our guide on choosing an equipment maintenance management system.

The four boundaries that separate measurable failures from unmeasurable ones

Once you have separated “failures that appear in vibration” from the rest, there is a second layer of boundaries waiting. Even the same bearing damage stays invisible if the conditions are not right. What decides this in practice is not the accuracy specification on the datasheet. It is the following four boundaries.

Boundary 1: velocity or acceleration (the measured quantity)

There are three main physical quantities used to measure vibration. Displacement (μm) is the amplitude of the movement itself. Velocity (mm/s) is how fast the movement is. Acceleration (G or m/s²) is the intensity of the movement. For the same vibration, low frequency phenomena tend to show up in displacement and velocity, while high frequency phenomena tend to show up in acceleration.

The evaluation metric used by ISO 20816 is vibration velocity in mm/s RMS, and the typical measurement frequency range is 10 Hz to 1,000 Hz. That band suits low frequency phenomena that live around shaft speed: unbalance, misalignment and looseness.

Early spalling in a rolling element bearing is a different animal. Every time a rolling element passes over a microscopic defect, it excites a high frequency resonance, a ringing, and the amplitude of that ringing is modulated at the bearing defect frequency. The energy involved is small and the frequency is high. In other words, the overall velocity value barely moves at all. If you are watching a velocity-based criterion and nothing else, your indicator stays flat until the bearing has visibly deteriorated.

This is where envelope analysis comes in. Apply a high pass filter to extract the high frequency content, demodulate it, and the defect frequency shows up as a peak before the overall vibration rises, often weeks earlier. Better still, the frequency at which the peak appears tells you which part is damaged. The idea is to check which of the four bearing defect frequencies the peak matches: BPFO (outer race), BPFI (inner race), BSF (rolling element) and FTF (cage).

Envelope analysis is said to require sampling on the order of 40 to 100 kHz. Among wireless sensors there is a class of products marketed as supporting high speed sampling, some of them offering sampling on the order of 26.8 kHz. The practical takeaway is this: a sensor that only transmits an overall velocity value and a sensor capable of envelope analysis do not see the same set of failures. That difference belongs in the specification discussion, not in a footnote.

There is one more thing that causes misjudgement on the shop floor with some regularity, and that is confusing g (acceleration) with mm/s (velocity). Someone misreads the unit of a number on a dashboard and compares it against the judgement values in a standard. Or a value captured in acceleration gets mapped onto a zone table built for velocity. Different units mean different meanings. Checking that the displayed unit and the unit behind the judgement criteria are the same is something to do before touching any threshold.

Boundary 2: rotational speed (slow machines do not register on velocity criteria)

For the same physical amplitude, vibration velocity is lower when the machine turns more slowly. On low speed machines the velocity value itself comes out small, so the machine simply never registers on a velocity-based judgement. Agitators, the output side of a gearbox on a slow conveyor, and some large fans fall into this group.

ISO 20816-3 covers industrial machinery above 15 kW and in the range of 120 to 30,000 r/min, and deals with evaluation in the in-situ installed condition. Outside that range, and particularly at the low speed end, it is a stretch to talk about good and bad using a velocity criterion alone. You end up needing acceleration and envelope analysis to look directly at the bearing defect frequencies.

The check on site is simple. Make a list of the rated speed and the shaft power of every machine you have nominated for monitoring. That alone separates the machines you can judge on a velocity criterion from the machines you cannot. Skip this sorting step and roll out the same sensor and the same threshold template across every machine, and the slow speed machines will sit permanently in “zone A” while they quietly deteriorate.

Boundary 3: mounting method (magnet and stud mounts give you different usable bandwidth)

The thing to decide before you select a sensor is how it will be mounted. The upper frequency limit you can actually use depends on the stiffness of the mounting. A magnet mount is easy to attach and remove, but it lowers the usable upper frequency limit. If you are aiming at high frequency content, you need a stud mount.

Get the order wrong and this is what happens. You select a mid-range sensor capable of envelope analysis, then stick it onto the bearing housing of an existing machine with a magnet. The datasheet may claim a wide bandwidth, but the mounting constrains the band, so the performance you paid for is not available to you. Choosing a sensor before deciding the mounting method means the bandwidth you bought is bandwidth you cannot use, and this is a failure that shows up particularly often in retrofit projects.

Retrofitting onto existing equipment brings in other conditions as well. Does drilling and tapping a mounting hole conflict with a warranty or with an agreement with the machine builder? Is the location in a hazardous area? Is the surface painted? Can a person physically reach the bearing housing? On top of all that, the mounting point is assumed to be close to the bearing and in a position where the load direction is known. Stick a sensor on an outer cover panel and what you are measuring is the vibration of the panel. Our general thinking on retrofitting sensors onto installed machinery is collected in IoT enablement and retrofit for legacy equipment.

Among the barriers to adopting predictive maintenance, it has been pointed out that on top of the initial investment in sensors and IoT infrastructure, cabling work and mounting work arise separately. Choosing wireless sensors reduces the cabling work, but it does not reduce the mounting work, the power supply work or the antenna work to zero.

Boundary 4: communications and battery (what interval measurement can and cannot tell you)

Wireless, battery powered sensors are not continuous monitoring. Battery life depends on the sampling interval and the transmission frequency, and is generally put at 2 to 5 years (3 to 5 years under favourable conditions). Continuous monitoring at one second intervals drains a battery quickly, so most wireless systems are built around interval measurement every 10 minutes to 1 hour, with a mechanism to go and fetch detailed high frequency data when an anomaly is detected. In the LoRaWAN family there are products that claim 10 years.

The conclusion that follows from those specifications is unambiguous. What interval measurement gives you is the trend of deterioration, not the instant of a micro-stoppage. A measurement taken once every 10 minutes cannot capture the moment of a workpiece jam that is cleared in a few tens of seconds. If you enter the project expecting that “fitting vibration sensors will reduce our micro-stoppages,” the expectation is already misaligned at this point.

Batteries also come back as an operating cost, without fail. If a mass replacement falls due in year 3, the labour and the cost spike in that one year. And the more measurement points you have, the more the replacement work itself becomes a load on the maintenance team. In the calculations later in this article, the battery replacement cost is spread across five years and included in the annual figure.

The wireless environment is a boundary in its own right. In a plant full of metal structures you get attenuation and reflection, and finding a workable location and a power supply for each gateway becomes a real constraint. This is not something to settle on a drawing. The number of gateways only becomes firm after a radio survey on site.

Start from the ISO 20816 zones for judgement criteria, but do not stop there

Setting thresholds from scratch on your own is not realistic, so use an international standard as the starting point. ISO 20816 is the international standard for the measurement and evaluation of vibration on rotating machinery, and its evaluation metric is vibration velocity in mm/s RMS. It expresses how violently the machine is vibrating, independent of frequency.

The zone A to D values

In ISO 20816 the zone boundaries change according to the machine group and the support condition. The values in the source table are as follows, in mm/s RMS.

The meaning of the zones is usually summarised as follows: A corresponds to a newly commissioned machine, B is acceptable for long term continuous operation, C is not suitable for long term continuous operation and calls for countermeasures to be considered, and D is capable of causing damage. This four-stage explanation is a general description. For the details of the classifications and the conditions under which they apply, please refer to the text of the standard itself.

As the table shows, the same value of 4.5 mm/s is the B/C boundary for Group 1 with rigid support, the C/D boundary for Group 2 with rigid support, and the B/C boundary for Group 2 with flexible support. Until the machine group and the support condition are fixed, the same number carries completely different meanings. Whether a machine is mounted directly on its foundation or sits on anti-vibration mounts or a base frame changes how the support condition is treated, so this is work that has to be done by looking at the actual installation.

Why zone judgement arrives too late for early bearing damage

Zone judgement is a criterion applied to an overall velocity value. As described under Boundary 1, early spalling in a rolling element bearing is a small, high frequency impact that hardly moves the overall velocity value at all. In other words, a machine can sit in zone A or B while damage is progressing inside the bearing. By the time the velocity value climbs into C, in many cases the damage is already well advanced.

None of which makes zone judgement useless. It is effective against the low frequency faults, unbalance, misalignment and looseness, and it is usable for assessing initial values right after installation. The practical arrangement is a two-tier one: zone judgement for the low frequency side, envelope analysis for early bearing damage. Take only one of the two and you will drop the failure mode belonging to the other.

Why baseline capture and threshold tuning take real working hours

The zone boundaries in the standard are a general starting point, nothing more. Two pumps of the same model will have different normal vibration levels if the installation, the pipe stress, the foundation or the load conditions differ. You only find out what normal looks like for a specific machine by running it.

What that calls for is baseline capture on a machine by machine basis. Collect data for several weeks to several months in a state you can reasonably judge to be healthy, understand the spread of the normal condition, and then place your own thresholds with reference to the zones in the standard. If a machine is subject to load swings or seasonal temperature variation, those swings need to be built into what counts as normal.

Among the difficult parts of predictive maintenance, the difficulty of having a person set thresholds from historical failure data has been raised specifically. Early systems had no AI learning and treated a threshold exceedance as a sign of impending failure just as it was, and there are cases where adoption failed for that reason. Operating this kind of system requires a certain level of specialist knowledge, and it takes time for maintenance staff to acquire it. For that reason, a small start limited to critical equipment is recommended.

That recommendation to narrow the scope lines up with the calculations later in this article. For the broader picture of predictive maintenance and how the systems are put together, see our guide to introducing a predictive maintenance system alongside this piece.

Costs fall into four layers (the sensors themselves are 47% of the five-year total)

Looking at a quotation as a single bottom line tells you nothing about what is driving it. Split the cost of vibration monitoring into the following four layers and the structure becomes visible.

• Layer 1, sensors – the sensor hardware itself. Unit price multiplied by the number of measurement points.
• Layer 2, collection infrastructure – gateways, power supply work, antenna work. Driven by the building layout and the radio environment rather than by the number of machines.
• Layer 3, software – visualisation and diagnostic software. Most of it is billed annually per measurement point, so it does not appear in the initial cost at all.
• Layer 4, people – external engineers during commissioning, in-house maintenance staff attending and configuring, and then the monitoring and judgement hours once the system is live.

For reference, publicly available overseas sources put the going rate for wireless vibration sensors at 150 to 350 USD per point for entry class devices, 400 to 800 USD per point for mid-range devices with FFT capability, and 900 to 1,800 USD per point for ATEX or Class 1 Div 2 hazardous area devices. Local prices shift with exchange rates and import conditions, so we are not converting these into THB here. For the calculations that follow, we use 18,000 THB per point as the model unit price, assuming a mid-range device.

For Scenario A (full deployment at 30 points), described below, the five-year total for equipment, software and commissioning work, excluding the maintenance hours spent on monitoring and judgement during the operating phase (45,760 THB per year, or 228,800 THB over five years), adds up as follows.

Against that 1,158,000 THB, the 540,000 THB of sensor hardware accounts for 46.6%. In other words, the sensors themselves are just under 47% of the five-year total, and the remaining 53% and change sits outside the hardware.

Be careful about the denominator. The denominator behind that 46.6% is the 1,158,000 THB that excludes maintenance hours. The true five-year total including the maintenance hours for monitoring and judgement is 1,386,800 THB, and on that basis the sensor share is 38.9%. The same phrase, “the sensor share,” produces different numbers depending on whether labour is in or out. When you put a figure like this on the table in a quotation review, it causes less confusion if you state which denominator you are using before you say the number.

Whichever denominator you look at, the message is the same. Negotiating 10 to 20 percent off the sensor unit price moves the overall total by a limited amount. What moves it is the number of measurement points itself, along with the infrastructure, the installation work and the operating hours.

Payback is decided by the number of vibration-visible failures, not by the number of machines

Here is the core of the argument. When you put a value on downtime reduction, the thing you compare against has to be fixed to a single baseline, or the numbers get counted twice. Below we place two options on the same model plant, full deployment at 30 points and a focused deployment at 12 points, and compare both of them against the same baseline, which is doing nothing. We do not subtract option A from option B.

Annual losses in the current state (doing nothing)

These figures assume 8 unplanned stoppages per year across the 15 candidate machines, an average of 6 hours per event, and lost profit of 12,000 THB for every hour the line is down. This 856,000 THB per year is the baseline for every comparison that follows. Scenario A and Scenario B are not subtracted from each other. Each is evaluated on how much it reduces this 856,000 THB.

Scenario A: full deployment at 30 points

This option puts sensors on all 15 rotating machines, 2 points each, one on the driver and one on the driven machine, for a total of 30 points.

Initial cost

Operating cost (per year)

The five-year total is 861,000 + 105,160 x 5 = 861,000 + 525,800 = 1,386,800 THB.

Benefit (difference against the baseline)

We assume that the 4 detectable events change from unplanned stoppages into planned stoppages, and that the downtime per event shortens from 6 hours to 1.5 hours.

• Reduction in downtime: 4 events x (6 − 1.5) hours x 12,000 = 4 x 4.5 x 12,000 = 216,000 THB/year
• Reduction in premium costs: 4 events x 35,000 = 140,000 THB/year
• Annual saving = 216,000 + 140,000 = 356,000 THB/year
• Annual net benefit = 356,000 − 105,160 = 250,840 THB/year
• Payback period = 861,000 ÷ 250,840 = approximately 3.4 years

What must not be overlooked is the loss that remains after the system is live. 856,000 − 356,000 = 500,000 THB per year stays on the table. The 3 events that do not appear in vibration, and the 1 event that progresses too fast to catch, remain even with all 30 points deployed. The reaction “we put sensors on everything and it still goes down” comes straight out of this structure.

Scenario B: narrowing to 12 points

This option uses the stoppage history to pick the 6 machines where vibration-visible failures have actually occurred, and puts 2 points on each, for a total of 12 points.

Initial cost

Operating cost (per year)

The five-year total is 426,500 + 46,640 x 5 = 426,500 + 233,200 = 659,700 THB.

Benefit (difference against the same baseline)

Because 1 event occurs on a machine left outside the narrowed scope, the number of events detected in advance is 3 rather than 4.

• Reduction in downtime: 3 x 4.5 x 12,000 = 162,000 THB/year
• Reduction in premium costs: 3 x 35,000 = 105,000 THB/year
• Annual saving = 162,000 + 105,000 = 267,000 THB/year
• Annual net benefit = 267,000 − 46,640 = 220,360 THB/year
• Payback period = 426,500 ÷ 220,360 = approximately 1.9 years
• Loss remaining after implementation = 856,000 − 267,000 = 589,000 THB/year

The difference over five years

We line the two options up on cumulative five-year net profit against the same baseline (annual net benefit x 5 − initial cost).

The arithmetic in each case is: A gives 250,840 x 5 − 861,000 = 1,254,200 − 861,000 = 393,200 THB, and B gives 220,360 x 5 − 426,500 = 1,101,800 − 426,500 = 675,300 THB. The gap in cumulative five-year net profit against the same baseline is 675,300 − 393,200 = 282,100 THB, and the result favours Scenario B.

This needs to be read from both sides. A detects 4 events and B detects 3, so A detects more. The annual saving is also higher for A at 356,000 THB against B at 267,000 THB. And the loss remaining after implementation is smaller for A, at 500,000 THB per year against B at 589,000 THB per year. In other words, A wins on the absolute size of the benefit.

The reason B still wins on cumulative five-year net profit is that the cost difference outweighs the benefit difference. The difference in initial cost is 861,000 − 426,500 = 434,500 THB, and the difference in operating cost is 105,160 − 46,640 = 58,520 THB per year. Against that, the benefit of one additional detected event comes to 4.5 hours x 12,000 + 35,000 = 89,000 THB per year and no more. The picture is that you add 18 points to gain 89,000 THB a year, and to do so you keep paying 58,520 THB a year and put 434,500 THB down at the start.

Deploy more and the benefit is larger, but the investment efficiency drops. Which of the two you choose is a management decision, but both sides of the picture belong in the decision material. Simplifying it to “B is the better deal, so we will not do A” is looking at one side only, and so is simplifying it to “A detects more events, so we will do A.” Note again that these two options are not meant to be subtracted from one another. Read each of them as an improvement measured against the same 856,000 THB baseline.

To say it once more for the record, all of these are calculated estimates under this article’s model conditions. Lost profit per hour, the premium cost of an emergency, and the hourly rate for maintenance labour vary enormously from plant to plant. It is entirely possible that the ranking flips when you substitute your own numbers. What matters is not the amounts themselves but the way the calculation is constructed, with the number of vibration-visible failures as the numerator.

Sorting out the terms: preventive maintenance, predictive maintenance and CBM

When an internal discussion fails to converge, the definitions are usually the problem. The reason people keep searching for the difference between preventive and predictive maintenance is that the two get mixed up in practice. Let us sort them out.

Preventive maintenance is the broad category set against breakdown maintenance. It refers to the whole idea of intervening before something breaks, and it contains both time based maintenance and condition based maintenance within it. Most of what is actually carried out in factories under the name of preventive maintenance is time based maintenance, that is, work performed on a cycle: regrease every 6 months, replace the bearing every 2 years.

CBM, condition based maintenance, decides on condition rather than on a cycle. You watch indicators such as vibration, temperature and current, and you intervene when a criterion is crossed. Because it judges the present state, it is a statement about “now.”

Predictive maintenance reads ahead from how the condition is moving. From the slope of the trend you estimate roughly when the machine will reach its limit, and you slot the repair into the next planned shutdown. Because it is a statement about “the future,” it needs accumulated data rather than a single measurement.

Machine diagnostics with vibration sensors naturally follows a sequence in implementation: start with CBM, and move closer to predictive maintenance as the data accumulates. Calling it predictive maintenance from day one means asking a system to predict without any baseline, and that tends to collapse into an operation that merely renames a threshold exceedance as an early warning sign.

The practical value of keeping the terms separate lies in investment decisions. Replacing time based maintenance with condition based maintenance means you stop discarding parts that still have life in them, and conversely you can act early on machines that break between scheduled intervals. But that argument only holds for machines whose condition shows up in an indicator. Trying to put a failure mode that does not appear in vibration onto a CBM footing leaves you with no indicator to switch to.

Additional considerations for factories in Thailand

When you plan vibration monitoring in Thailand, there are points where carrying over the assumptions used in Japan is not enough.

Environmental conditions. High heat, high humidity and dust feed directly into the ingress protection rating of the sensor and the choice of installation location. In areas that are effectively outdoors, in areas subject to washdown, and in processes handling powders, the protection rating and the operating temperature range belong at the top of the specification checklist. In addition, in an environment with a high ambient temperature, the meaning of watching vibration and temperature together changes, because you now have to separate a temperature rise caused by bearing deterioration from one caused by the surrounding environment.

The radio environment. Steel frame buildings, metal ducting, partitions, and coexistence with existing wireless LAN and handheld terminals. The location and the number of gateways are decided by a radio check on site, not by a drawing on a desk. The calculations above assume 3 gateways for 30 points and 2 for 12 points, but that is a part of the model that will quite normally change with the building layout. The same goes for power: if a gateway is going up near the ceiling, power supply work comes with it.

Turnover among maintenance staff. Running vibration diagnostics takes a certain level of specialist knowledge, and it takes time for maintenance staff to acquire it. If the judgement criteria and the history are not handed over when a person changes role, you end up with one of two outcomes: thresholds that nobody dares touch, or alerts that everybody ignores. A design that keeps the basis for each judgement in the system rather than in a person’s head pays off here even more than it does in Japan. The baseline record, the change history of each threshold, and the record of what was actually done in response to each alert. Vibration monitoring software on its own often cannot hold all of that, so it is something to think through together with the maintenance management side.

The level of labour costs and electricity prices. Thailand’s minimum wage for 2026 is reported to be held unchanged. Monthly wages for manufacturing workers are 437 USD in Thailand against 302 USD in Vietnam, which puts Thailand roughly 45% higher. Given that the hourly rate for maintenance labour has a real effect on the numbers, how many hours a week you spend on monitoring is not a cost you can ignore. The calculations set Scenario A at 2 hours a week and Scenario B at 1 hour a week because more measurement points mean more things to look at. On electricity, as of 2024 the commercial tariff is reported to be in the region of 4.3 baht per kWh, including transmission, distribution and taxes.

The investment context. Investment applications in Thailand in the first half of 2026 came to 43.6 billion USD across 1,299 cases, up 37% year on year. Under the BOI’s Smart and Sustainable Industry category, 132 cases worth 507.6 million USD were reported as applications covering machinery renewal, adoption of digital technology, and the introduction of automation and robotics. For investments that involve equipment renewal or digitalisation, frameworks of this kind can come into consideration. Whether they apply depends on the specifics of each case, so treat this as an item to confirm during the planning stage rather than a benefit to assume.

A 90-day implementation sequence

Assuming you take the narrowing approach, here is a realistic way to proceed.

Weeks 1 to 2: count how the plant actually stops. Gather the stoppage records for the past 12 months and classify each event by failure mode. Splitting them into just two groups, those that appear in vibration and those that do not, is enough. Tie the number of events, the downtime and the repair cost to each. The deliverable at this stage is a single table. If the result shows that almost none of your failures appear in vibration, then there is a legitimate decision to put the vibration sensor investment on hold and move the budget to a different countermeasure. Keeping the process capable of accommodating that branch is important.

Weeks 3 to 4: decide the target machines and the mounting points. Take the machines where vibration-visible failures have actually occurred as candidates, and list their rated speed, shaft power, machine group and support condition. In parallel, decide the mounting points, close to the bearings, and the mounting method, magnet or stud, by looking at the physical machines. Only at this point does the measured quantity and the required bandwidth become fixed.

Weeks 5 to 6: decide the sensor specification and the collection infrastructure. From the bandwidth you have settled on, allocate machine by machine whether an overall velocity value is sufficient or whether envelope analysis is required. In parallel, check the radio environment on site and fix the position and number of gateways along with the scope of the power supply and antenna work. Only at this stage does a quotation match reality.

Weeks 7 to 10: installation and baseline capture. Mount the sensors, confirm communications, and accumulate data from normal operation. Do not make judgements during this period. This is the window for seeing how load variation and different operating patterns show up in the vibration values.

Weeks 11 to 13: threshold setting and operating rules. Set the thresholds with reference to the ISO 20816 zones and in light of the spread of the normal condition on each machine. At the same time, decide who does what and within how many hours when an alert fires, where the record of each judgement is kept, and who approves a change if someone wants to move a threshold. A situation where alerts flow but there are no operating rules is the entrance to “it fires so often that nobody looks.”

There is no requirement to cover every machine in 90 days. In fact, running one full cycle on a narrowed set of machines and firming up the operating rules before expanding to the next group makes the learning from threshold tuning far more useful.

Frequently asked questions

Will fitting vibration sensors reduce our micro-stoppages?

It depends on what is causing them. Most wireless vibration sensors take interval measurements every 10 minutes to 1 hour, so they cannot capture the actual moment of a workpiece jam or a false sensor reading that is cleared within a few tens of seconds. What vibration monitoring can reduce is the unplanned stoppages caused by deterioration in rotating machinery. For micro-stoppages, putting a mechanism in place first to record the reasons for each stop tends to be the shorter route.

Can sensors be retrofitted onto existing equipment?

In many cases yes, but there is an order to the decisions. First decide the mounting point, close to the bearing, and the mounting method by looking at the actual machine, and select the sensor after that. A magnet mount lowers the usable upper frequency limit, so if you are aiming to catch early bearing damage with envelope analysis, a stud mount is required. Buy the sensor first and you end up in a state where the mounting constrains the bandwidth and you cannot use what you paid for. For retrofits, whether a mounting hole can be drilled and tapped, the installation environment, and securing power and antenna locations are all items to confirm in advance.

Can we detect anomalies on low speed machines?

There is a region where a velocity criterion in mm/s alone is difficult. At low speed the vibration velocity itself is small, so the machine does not register on ISO 20816 zone judgement. ISO 20816-3 covers industrial machinery above 15 kW and in the range of 120 to 30,000 r/min. For slow machines outside that range, consider a configuration that goes directly after the bearing defect frequencies (BPFO, BPFI, BSF and FTF) using acceleration and envelope analysis. As a first step, list the rated speed and shaft power of the candidate machines and sort them into the group you can judge on a velocity criterion and the group you cannot.

How many measurement points is a reasonable starting number for motor vibration monitoring?

Do not start from the number of machines. Work through the stoppage records for the past year, identify the machines where vibration-visible failures have actually occurred, and narrow the scope to those. Under this article’s model conditions, the option narrowed to 6 machines and 12 points (cumulative five-year net profit 675,300 THB) came out 282,100 THB ahead of full deployment across 15 machines and 30 points (393,200 THB). The structure is that full deployment detects more events (4 against 3), while the benefit of that one additional event, 89,000 THB per year, does not cover the cost difference of 434,500 THB up front and 58,520 THB per year. The numbers change from plant to plant, but the way the calculation is assembled transfers.

How long do the batteries last, and how should we allow for replacement?

Battery life depends on the sampling interval and the transmission frequency, and is generally put at 2 to 5 years (3 to 5 years under favourable conditions). Continuous monitoring at one second intervals drains the battery quickly, so most systems are built around interval measurement, with detailed data captured when an anomaly is detected. In the LoRaWAN family there are products that claim 10 years. On the cost side, it is safer to carry the replacement work in operating costs. In this article’s calculations, Scenario A carries 30 points x 900 THB = 27,000 THB in year 3 and Scenario B carries 12 points x 900 THB = 10,800 THB, each spread over five years, giving 5,400 THB and 2,160 THB per year respectively. The fact that the replacement work itself becomes a load on the maintenance team as the point count grows is another input when deciding how many points to install.

Summary

Machine diagnostics with vibration sensors is decided not by accuracy and not by AI, but by separating out whether the way a given machine fails shows up in vibration at all. What vibration shows you is four modes on rotating machinery, unbalance, misalignment, looseness and bearing damage. Electrical faults, control faults, fixtures and dies, workpiece jams and changeover errors are outside its scope.

Whether something is visible is then decided by four further boundaries: the measured quantity, velocity or acceleration; low rotational speeds that do not register on a velocity criterion; the mounting method that governs the usable bandwidth; and the constraint of interval measurement imposed by communications and batteries. These four decide what is actually possible far more than the accuracy specification in a catalogue does.

Build the investment case from event counts, not from machine counts. Under this article’s model conditions, against an annual loss of 856,000 THB from doing nothing, full deployment at 30 points saves 356,000 THB a year for a cumulative five-year net profit of 393,200 THB, while the option narrowed to 12 points saves 267,000 THB a year for 675,300 THB. The gap is 282,100 THB in favour of the narrowed option. That said, full deployment detects 4 events against 3, so the absolute size of the benefit is larger for the former. Whether you take benefit or efficiency is a judgement made with both sides of that picture on the table.

Look at the cost structure as well. Against a five-year total of 1,158,000 THB for equipment, software and installation work, excluding maintenance hours, the sensors themselves come to 540,000 THB, or 46.6%. The remaining 53% and change sits outside the hardware. Narrowing the point count and designing the operation are more effective levers than negotiating the unit price.

The first thing to tackle is a single table classifying the past 12 months of stoppage records by failure mode. How many of them are failures that vibration can see? Everything starts from that number.

TOMAS TECH is based in Bangkok and supports Japanese manufacturers across factory IT, OT and IoT, and FA. We are happy to be involved before the sensor selection and the quotation, at the stage of working through the stoppage records to separate out which machines fail in ways that vibration can see. We can also help you check the conditions for retrofitting onto existing equipment and think through how this connects to your maintenance management setup, based on the situation on your own shop floor. To get in touch, please use our contact page.