I have Maintenance/Reliability background & wish to understand/implement CBM with PI Products. I do not have the IT background. Where should I start?
Usman: Please look at:
UC 2015 talk: Keeping Assets Healthy - PI System’s role in Asset Maintenance Health
UC 2016 Lab:
Condition-based maintenance (CBM) is a strategy where you monitor the actual condition of an asset to decide what maintenance needs to be done – see wiki for a broad definition. We also covered CBM at OSIsoft Users Conference 2015 – see the talk "Keeping Assets Healthy – PI System’s Role in Asset Maintenance". In this lab we show how to use sensor data to implement basic CBM tasks identified in the above talk:
>Exercise 1: Usage-based maintenance – Motor run hours
>Exercise 2: Condition-based maintenance – Motor vibration high condition
>Exercise 3: Predictive maintenance – Equipment failure, anomaly detection and remaining useful life
You have several critical motors in the plant. Currently, you use calendar-based schedule (weekly, monthly, quarterly, half-yearly, yearly etc.) to do preventive maintenance (PM) inspections and minor repairs/service for these motors. The motors don’t run continuously - their usage depends on the production schedule. As such, a motor may be idle for several hours during a day and sometimes even for several days or weeks. However, since maintenance personnel don’t have visibility into their usage, motor PM tasks are performed even when they are not required – for example, weekly PM services on motors that have been idle for several weeks.
For more details and Hands-on Lab Notes, see http://cdn.osisoft.com/learningcontent/pdfs/TechCon2016_ConditionBasedMaintenancewithPIAF.pdf
UC 2017 Lab:
Incorporating Condition Monitoring Data - Vibration, Infrared and Acoustic for Condition-based Maintenance
Traditionally, the PI System has worked with process data from plant instrumentation such as PLC and SCADA. However, newer IoT and edge device capability allows you to bring data from condition monitoring systems such as vibration, infrared (thermography), acoustic etc. to the PI System. Take this lab to learn how to use condition monitoring data along with process data in your condition-based maintenance programs to improve equipment uptime. The lab will also include the use of alert roll-ups, watch lists, KPIs and others for a holistic view of asset health and reliability.
The learning objectives for this lab are:
In this lab, we will use a hand-held device to collect vibration, infrared, and acoustic data. The device is a Windows 10 based unit (it can also be an iPad) with suitable attachments based on National Instruments http://www.ni.com technologies. Please see AR-C10 for hardware details.
We will also incorporate CM measurements via devices from Fluke http://www.myflukestore.com/content/fluke_connect?gclid=CIec1-LUldMCFRBEfgod4ogIhA
The lab book is attached.
Re. integration with work management systems, we often get questions re. updating meter readings or triggering workorders in SAP/Maximo - these were covered in Hands-On Labs in previous years - those books are also attached to this thread.
And, the PI World 2018 CBM Hands-on Lab info is here:
CBM and Condition Monitoring – Process vis-a-vis Equipment Reliability
A great place to start is to check out the learning material at Learn PI. Another place that might be beneficial is to check out the Maintenance and Reliability group which is where people with your background can talk about PI and how to do exactly what you are asking.
If you don't find what you are looking for, please let me know and I'll try to find more information for you!
I will definitely have a look on the referenced material/links.
Thanks Gopal Gopalkrishnan,
I will definitely have a look on these labs. Will definitely bug you again if I get stuck somewhere
The lab that Gopal mentioned has a nice manual that can be downloaded here. The virtual machines can also be accessed but there is a subscription required for them, the subscription can be purchased here, either one of the first two subscriptions will give you access to the virtual machines.
Thanks Pablo for providing all this nice material. It will be very helpful for me.
Just some other thoughts that may help you. We held a webinar that specifically targeted folks like yourself. The link to this webinar is here. Please note that also at that link is a link to download our guidebook on CBM. It has a lot of information about how to get started.
Also, we have several YouTube videos on our learning channel with regard to CBM here.
It's best to have an idea in mind when understanding how to use PI in CBM scenarios. If you're looking for ideas, the webinar above is a good starting point. If you have an idea and want to know how to implement, the YouTube videos may help but you can also feel free to ask your questions here, in PI Square. We have industry groups too, so if you're in a specific industry, e.g. Power Generation, Chemicals, Gas & Oil, etc. perhaps joining one of those groups here on PI Square will also be helpful.
Hopefully these responses have helped. If so, please indicate which answers have helped so that others can find the right answer to the same question. If you need additional information, please feel free to post it here.
PI World 2019 (San Francisco) hands-on lab re. maintenance & reliability:
Maintenance and Reliability – Aligning asset maintenance with operations – failure modes, usage-based, condition-based and predictive (pattern recognition) maintenance
Day 3 and Day 4: Power User Lab
Table of Contents and Summary pages from the Lab workbook is attached. Complete Lab Workbook is also attached.
Increasing equipment uptime means preventing failures before they happen; and in turn this requires you to have the list of likely failures and the appropriate condition monitoring for the process or equipment/component. Attend this lab to learn about failure modes, and corresponding monitoring techniques to prevent failures. The lab will also cover the use of operations data for a layered approach to uptime and reliability via usage based, condition-based and predictive maintenance. Usage-based maintenance includes using operational metrics such as motor run-hours, compressor start/stops, grinder tonnage etc. And, condition-based maintenance utilizes measurements such as filter deltaP, bearing temperature, valve stroke travel, and others. Predictive maintenance can be simple predictive such as monitoring vibration (rms, peak etc.) to predict RUL (remaining useful life) or heat-exchanger fouling to schedule cleaning ; and advanced predictive maintenance use cases include pattern recognition or other machine learning techniques for detecting anomalies/predicting failures.
(Who should attend? Power User and Intermediate)
MFPT (Machinery Failure Prevention Technology) 2019 - Abstract and slides are attached.
Industrial sensor & IIoT data and meta-data management
Industrial sensor data is Big Data and largely of time-series nature for asset intensive industries such as oil & gas, chemicals, paper & pulp, pharma, mining etc., including utility companies in electricity, gas and water production/generation and transmission/distribution. Attend this session as we review the challenges in sensor and IIoT (Industrial Internet of Things) data and meta-data management.
This involves working with large volumes of high velocity data from automation and control systems with hundreds of thousands to millions of sensors; each sensor collecting measurements every minute, some every second or even few microseconds. The data may also be from CMS (condition monitoring systems) and several other disparate line-of-business data sources such as planning, work management, quality, weather, web pages etc. IIoT and edge devices now bring in even more data with their own new challenges during data collection, and the need to merge this data with legacy sensor data sets.
This presentation will focus on the lessons learnt during our 35+ years of working with data and information management in manufacturing operations. And, we will cover how data analytics (with machine learning when appropriate) and visualization are used in customer use cases in energy reduction, predictive maintenance and reliability, process yield improvement, product quality, and others.
A data infrastructure approach with a faceted and application-adapted digital model allows flexible and extensible methods for data collection, analysis, and visualization that are fit-for-purpose and finally, actionable.
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