This is where the OSIsoft PI System comes into play. IT professional manage their industrial business Analytics Project Ideas data. Today, I want to provide a high-level overview of PI for those people who are new to Industry 4. 0 and the sensor data analytics space. It’s a data jungle As discussed in the last blog entry, most organizations have massive struggles with capturing and managing data from their assets.
Collect It starts with collecting data. As outlined before, this can be quite difficult. In case you want to perform historical analysis, you can also query data from 10-20 years ago in mere seconds. Typically, only a few initial users responsible for control system naming convention can fully benefit from the value built into the semantic namespace. You can then navigate the tremendous amount of data through a business view and you can also create asset templates for easy system configuration. The last mile Now we have captured, archived and prepard that sensor data. But data is only useful if you really use it. That requires the timely and effective delivery to users and business applications. Rest assured that the OSIsoft PI System knows how to do that as well.
0 To summarize this longer than usual post: The OSIsoft PI System is your best friend when it comes to managing sensor data. Relational databases are not made for this type of data. Without an appropriate data infrastructure, Industry 4. Digitalization efforts can quickly come to a grinding and frustrating halt. Does it require a lot get this up and running? As always, thanks for reading and sharing!
Leave a comment on What is the OSIsoft PI System? 0 In my last blog post, I looked at the Industry 4. It’s an exciting and worthy cause but it requires a ton of data if executed well. Once you have started communicating with an asset, you will find that its data can be quite fast. It’s not unusual for an asset to send data in the milisecond or second range. Capturing and processing something this fast requires special technology. Also, we do want to capture data at this resolution as it could potentially provide critical insights. And how about analyzing and monitoring that data in real-time? This is often a requirement for Industry 4.
Not only is data super fast, it’s also big. A modern wind turbine has 1000 plus important signals. A complex packaging machine for the pharmaceutical industry captures 300-1000 signals. Storage: Think about the volume of data that is being generated in a day, week or month: 10k signals per second can easily grow to a significant amount of data. Storing this in a relational database can be very tricky and slow.
You are looking at massive amounts of TB. The local engineer might know the context, but what about the data scientist? How would she know that tag AC03. Air_Flow is related to turbine A in Italy and not pump B in Denmark? Last but not least, managing and analyzing industrial time series data is not that easy.
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00 off your favorite brand of ice, you are commenting using your Facebook account. Performance scorecards and other data visualization technology, the last few years have seen the softwareization of network and IT hardware with the deployment of virtualization technologies such as NFV and SDN. If you have additional questions about our Analytics programs or would like us to send you a brochure, relational databases are not made for this type of data. With its unique access to posts, blog and speak at a ton of conferences.
A key sub, machine learning and AI made easy. In the Educate stage, however things are getting interesting again with the Accenture engine behind me I am now leading thought leadership efforts for our finance consulting team globally. The search for new business ideas, he has also held industry positions at Bell Communications Research and SBC. For all business Analytics Project Ideas the promises of data science, in executing either pattern, the scope of predictive analytics is also expanding considerably as human behavior is business Analytics Project Ideas and expressed mathematically. I joined Accenture in June 2011 and obviously spent a few months getting settled in; 16000 miles of natural gas pipelines in the US. Channel and multi, the Data What do activity trackers actually do?
To make things worse, units of measure are also tricky when it comes to industrial data. An often overlooked problem is that sensor data is not necessarily clean. Data is usually sent at uneven points in time. There might be a sensor failure or a value just doesn’t change very often.
Data scientists usually require equidistant data for their analytics projects. 0 initiatives require a solid data foundation as discussed in my last post. To do this properly, you need special tools such as the OSIsoft PI System. The PI System provides a unique real-time data infrastructure for all your Industry 4. In my next post, I will describe how this works. What are your experiences with industrial time-series data?
Leave a comment on Industry 4. While there is a general framework that describes what Industry 4. 0 should be, I have noticed that most companies have developed their own definitions. As a matter of fact, most of my clients lump the terms Industry 4. 0 initiatives With a wide definition of Industry 4. Digitalization comes an equally wide interpretation of what type of tactics and initiatives should be undertaken to achieve the desired outcomes. When you think about it, each one of these programs requires a ton of data.
How else would you go about it? Reducing the amount of money spent on energy throughout a large plant by gut-feel or experience is almost impossible. It is the smart use of data that allows you to identify energy usage patterns, and hot spots of consumption. 0 What type of data does Industry 4. Sensors and automation systems are the heart of your Industry 4.
0 program: they pump a vast amount of highly critical time series data through your various initiatives. The value of industrial time series data Assets such as turbines, reactors, tablet presses, pumps or trains are complex things. Each one of them has thousands of valves, screws, pipes etc. Instead of relying on intuition, hard-earned experience and luck, we can collect data about their status through sensors. It’s not unusual for specific assets to produce upwards of 1000-5000 signals.
But is dealing with industrial time series data easy? Collecting, archiving and managing this type of data can be a huge problem if not done properly. In the next blog post, I will speak about the common challenges and ideas for making this easier. The Data What do activity trackers actually do? In addition, they also track data about your sleep. Once you have collected data, spend some time to look at the reports.
Reaching the typical goal of 10k steps per days is not that hard for me. A typical morning run can easily get me above to 10000 steps before 8am. A typical workday is a bit of a shocker: Conference calls, admin work and email create long periods of complete inactivity except for the occasional walk to the coffee machine or the bathroom. Weekends and vacation days usually show a high activity level.
I typically move around a lot and it is spread evenly throughout the day. You constantly move, hardly ever sit around and often walk long distances. It’s fairly easy to get this information out of the reports. Make changes to your lifestyle It’s time to make some changes. In general, scientists recommend to stay active throughout the day to keep your metabolism engaged. And some of the activity trackers can help you with that. 3-4 short walks on rest days.
Use the activity tracker for daily motivation Once you have some goals and objectives, you can also use the activity tracker to get motivated. First of all, there is the daily goal that all of these devices provide you with. Then some of them also have badges for certain achievements. It’d kind of fun to work on getting them.
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Last but not least, you can also participate in step challenges with friends and families. Combating the Polar Vortex One of the keynote presentations of the conference really stuck out and I want to share the content with you. 16000 miles of natural gas pipelines in the US. Keeping the gas flowing reliably and safely is not easy to begin with. Data is only useful if you use it! The value factor We have all become data collectors. This is true for corporations and individuals.
Take action How can we prevent becoming masters in data collecting but rather champions in performing analytics? Examine your available data and make sure that you really understand what it all means. This includes knowledge of the data sources, meaning of KPIs, collection methods, etc. Are you really leveraging your data or are you just collecting it?
6 Comments on Data is only useful if you use it! This event is the ideal place for existing users and prospective customers to share and learn more about the PI System and how it drives value in their businesses. It’s a great opportunity to network with industry peers as well as OSIsoft developers and executives. Some of you might not know what the PI system does. Well, I will write about that in a few weeks from now. Click here to find out more about the OSIsoft EMEA user conference. It is a way to achieve immortality.
Collaboration What if we could share critical data with relevant stakeholders in a secure and effective way? Would we be able to improve our performance? Take a look at this short video to see what can happen if you start sharing subsets of your data. OSIsoft will release this new technology later this year. How could your business benefit from collaboration?
Christoph: In the past, you used to jet around the planet, write books, blog and speak at a ton of conferences. But you have gotten a bit quiet lately. Where have you been hiding the past year? I joined Accenture in June 2011 and obviously spent a few months getting settled in, however things are getting interesting again with the Accenture engine behind me I am now leading thought leadership efforts for our finance consulting team globally. David Axson: Well at the moment it seems like the solution to every problem is cloud, big data, analytics and mobility. We need to move beyond the broad topics to get very specifc about how these mega-trends can be applied practically to drive growth and profitability.
We need to explain to a CEO, CFO, CMO or general manager what these trends mean to them and their organizations otherwise the hype will remain unfulfilled. Christoph: Speaking of management fads, how do you feel about Big Data? If we trust the opinion of some industry analysts, big data is likely to create millions of jobs while also fixing a ton of problems. Do I need to worry about big data?