Set Warning Alarms the quick and easy way

Format Trend data – where does it come from, where is it stored. Philosophy of alarms – why have them, what are they trying to achieve. How the warning alarms are calculated and how to set them. How to modify them.

Trend data and alarms Where does the trend data come from Where is it stored

Trend data The trend data to be captured is defined in the AP set

Spectral data is captured, trends are calculated

Trend data can be graphed

The trend data is stored in the database

Well, so what? So what is the point of knowing where this data is stored. It is from this data that the warning alarm is calculated At this point, I would like to discuss the philosophy of alarms.

Alarms – Why have them? The reason for an alarm is to notify a departure from “normal”. The theory is that this allows data to be screened, without having to manually wade through thousands of points. Allows more time to be spent analysing “faults”

Statistically based For the vast majority of cases, statistical analysis is the basis for setting alarms (Autostat is based on this) The problem arises when there is a wide variance of parameter trend values, even among similar machines Because the Alert and Fault limits in the Alarm Limit set are “fixed”, a different alarm limit set will be needed if the trend values vary significantly compared to other points or machines. In this case, a huge number of Alarm limit sets are required, and housekeeping and administration becomes arduous

An example of trend variations Consider the drying section of a paper machine Consists primarily of felt rolls and drying cylinders

There are 100 felt rolls in the machine. There is an enormous variation in the vibration characteristics of the felt roles. Doesn’t seem related to their position. Trying to set alarms manually for these rolls is very difficult and time consuming Trying to manage/maintain/modify them is also very difficult and time consuming

Consider the following two spectrums These are from felt rolls that are in close proximity Each have very similar overall level Trend values are substantially different To effectively apply alarms, I would need two different alarm limit sets

If an Alarm Limit set is assigned to more than one point, a change to the Alert or Fault level will change for ALL points to which the set is attached. If this happens, the Alert and Fault levels need to be checked against all the points to which that alarm limit set is assigned. Or, a new/different AL set needs to be assigned. Only 512 AL sets allowed per database, and we have 3000 points in some of our databases

I initially tried to group points based on historical vibration levels Export data into excel Group based on AP set Sort based on Overall, then parameter 1, then parameter 2 etc Still gives a range of data Significant number of spurious alarms from data at the high end of the range

Philosophy??? Here comes the contentious bit….. There is an easy way to have a meaningful and sensitive alarm, that is tailored to each individual trend. This method relies upon the analyst being able to determine severity, and not rely on alert and fault alarm limits

Neat and tidy This method can effectively dispense with Alert and Fault levels. It relies on the warning alarms to alert the Analyst to any variations from “normal”. It relies on the Analyst doing the analysis. You can have the same number of AL sets as AP sets It’s easy to manage

Alarm Limit Set

Warning Alarm values Where do the alarm values come from? Alert and Fault alarm values are the numbers in the Alarm limit set. They are “fixed” The Warning alarm value is calculated from parameters stored in the database, and is calculated for each individual trend.

Warning Alarm Value The warning alarm value is –The Baseline x the baseline ratio OR –The Average + (Bs x Standard Deviation) The lesser of these two calculated numbers is the value used as the warning alarm

Consider the Overall trend: Baseline ratio (Br from the AL set) = 2.5, Baseline value (from Trend data) = Thus, the “Br” warning alarm value will be x 2.5 = 6.19 OR Average (from trend data) = Standard deviations (from trend data) = Maximum deviations (Bs from the AL set) = 3 Thus, the “Bs” warning alarm value will be (3 x 0.584) = The lower value is the alarm level used, so the warning alarm will be 4.228

The baseline value (in the database) is less than: –The Fault Alarm value x “Percent of Fault Limit for Baseline Override” In which case, this value will be substituted for the baseline value for the purpose of calculating the warning alarm UNLESS

The “Percent of Fault Limit for Baseline Override” is found in “Database Setup” in the main menu, under database global information.

In the previous example: From the AL Set; –Overall Trend parameter has a fault level “D” value of 3.5mm/s From the database set-up: –% of fault limit for baseline override = 10 From the Trend data: –Baseline value = 2.476mm/s Check: 10% of 3.5 = 0.35 This is less than the Baseline value, so the baseline value is used

How to modify and set the Average and Sigma, Baseline This procedure relies on good, consistent historical data There are some traps to be aware of Two parts; –Calculate the Average/sigma and Baseline for the entire database, and –Modify single points

Calculate statistics for the entire database

The average and standard deviation can be recalculated, and the baseline re-assigned for any date range specified for the entire database

Calculate new statistics The Average and Standard Deviation have now been re-calculated for the entire database

I recommend setting the baseline to be equal to the average This avoids possible spurious results It allows you to see the last time that the database statistics were calculated Baseline value

Set Baseline equal to Average The baseline value is now equal to the average for the entire database

Modify individual machines Same process as for the entire database, but just highlight the machine/area required

The data can also be manually edited

This data can be manually edited That is, you can type whatever numbers you like in here!!

By using the method so far described, warning alarms that are sensitive and specifically tailored to each trend value are used. Since the warning alarms are the most sensitive and reliable, I recommend disregarding the Alert and Fault alarms Alarm Limit sets

This allows the alarm limit sets to be rationalised. I have the same number of alarm limit sets as AP sets Arbitrarily set the Fault level to 10 for all parameters Set the alert level to 0 Set the Baseline ratio to 1.85 Set the “percent of fault limit for baseline over ride” to 0%

Why set the fault alarm to 10? The order the software checks alarms is as follows: If the Fault level is breached, an alarm is generated. The software doesn’t check any other alarm levels If the Fault alarm is not breached, the Alert alarm is checked, and so on. However, if the warning alarm value is HIGHER than the fault alarm value, then the warning alarm is ignored.

Why set the fault to 10? Most parameter values will be less than 10 If the trend value exceeds 10, you probably need to do something anyway You can use any value you like, provided it is representative of your data.

Exceptions reporting Now you are ready to run an “Exceptions Report”

Alarms activated D alarm is the “Fault” C Alarm is the “Alert” B Alarm is the “Warning”

Generate an Exceptions report

Exceptions Report

Things to be wary of Slowly rising trends can catch you out In the example following, the statistics were calculated across an 18 month period from March 03 to Sept 04 This gave an average of about 0.7 mm/sec which set the alarm to about 1.3 mm/sec. Too high When the new bearing was installed, the baseline was manually reset to 0.2 mm/sec.

In Summary To set reliable warning alarms, recalculate the database statistics. Baseline = Average Baseline Ratio to 1.85 in the AL set Fault alarms to 10 Alert alarm to 0 Percent of fault limit for baseline override to 0% Good Luck

Questions

Lubrication

Using Vibration Analysis to Identify Lubrication issues PeakVue HFD Headphones

PeakVue Single average Noisy spectrum Noisy, random waveform Auto-correlated waveform shows no patterns

Time Domain Waveform Before After Frequency domain Spectrum Before After

Auto- correlate

Alarms? If 3-12kHz (HFD) is in alarm, and if PeakVue is also in alarm, and no other trends have breached alarms it’s a fair bet that the bearing might need some grease.

We used to take a 10kHz spectrum Found that PeakVue was just as effective Identified grease was not performing Identified ancient grease porting to some bearings

Questions Thank You