Monthly Archives: August 2017

Retrospective assessments before moving to predictive alerts #DataAnalytics #EnergyAnalytics

Greetings!

I was recently onsite for a data discovery exercise, unit has one of the largest single location manufacturing capacity in its sector. Of a lot of data sets we looked at, one of the interesting case that came in front of us was that of a vibration of a Fan, one that is very important in the entire process, lowering of the operational RPM could result in significant production loss.

We took past data sets and wanted to understand how the retrospective assessment is done by the team, as expected a lot of time went into fact finding and was dependent on a lot of people. Besides taking time, no one could point out exactly when the issue started building up and when would have been the right time to respond to it?

What did our algorithms (series of logics, no ML really) find out?

1.       Total of 1953 peaks happened, where in the rise in vibration % was such that if continued it could have mean an X% increase over 24 hours.

2.       1606 cases where the peaks where in consecutive points the vibration increased by 50% of X%, we have called them as Alerts. (In the current scheme of things alarm only goes when things are out of control)

3.       823 out of 1606 cases had consecutive alerts, in quite of a few of them 4-5 alerts came in successively. (Remember these alerts are not simply a>b “raise alarm”, it tracks the tendency and past pattern)

4.       There were 7 occasions (exact date and time pointed out) when plant had an unplanned shutdown (over 8 hours) and the problem could have been addressed. (Next time when that happens an maintenance team already has a ticket to address the issue)

5.       Algorithm automatically pointed out how maintenance activity in one of the cases could normalize the increase in vibration %, while in the other they couldn’t or perhaps no action was taken. (So if a ticket is marked resolved and technically the problem stays, the algorithms points out it close to real time)

6.       Because of last two tickets going un attended the unit lost out 7% production over a stretch of 5 weeks and had to wait for another unplanned shutdown to address the issue!

Point 1 to 6 happened even after people were looking at the screens 24X7! Time to have people taking actions and not looking at screens, real time monitoring is a thing of past, but to move to future the team needs to of adequate tools to do retrospective assessments and eventually go on to work on systems/tools that predict an anomaly building up well in advance!

Well that’s a real case study! Liked it? Would love to hear your views/thoughts!

Best Regards,

Umesh Bhutoria

Investing in Data Management Application? Have you considered these 3 points?

Greetings!

Over the last few months we have seen organisations investing or deciding to invest in suite of web and mobile applications to manage data and automate part of reporting process when it comes for scaled #EnergyEfficiency or #CleanerProduction focused programmes.  3 things that one must consider before deciding in selecting the right vendor:

1. Thought Leadership

Use of #AI or simply put series of logics to automate certain processes is evolving, there is a lot of noise when it comes to people talking about it. One must decide to work with partners that have worked on similar applications before and have had the habit of innovating in the domain.

2. It’s not about IT

Developing an application or designing a form is not the important part. What the system does and how it helps is important? Some of the potential benefits that must come from such a system is reduced project management costs, standardisation etc? So if your vendor has not delivered it before there are chances that it they might fall short again.

3. Business Model Innovation

Such applications have to evolve every day so that they can last for 4-5 years, hence it is important to consider innovative business models before embarking on the “product” development. Vendor with core interest in such applications is best suited as against to a conventional “Developer”

EnergyTech Ventures is an emerging company that has the largest portfolio in the #DataHub Space with it’s application in the #EnergyEfficiency and #CleanerProduction programmes being used by 100+ factories in 6+ countries. We have helped organisations reduce the operation costs by around 30% when it comes to data crunching, validation and reporting.

To know more about our work please visit us at www.entechventures.com 

Micro Services in #IoT #EnergyAnalytics

Greetings of the day!

A year back i saw a video of Steve Singh, he was talking on the context of "micro-services" and how large technology companies want to leverage that to have more customers and more importantly more revenue/customer.

That's when we started to work on our Energy Efficiency Micro-Services Hub (releasing on 8th September, 2017 on the sidelines of #EDASummit17). We open up our existing IPs (via APIs) as we continue to focus on developing more such IPs.

Interestingly our first #B2B engagement based​ (Cement Sector) on #API pricing has got a green signal. Our efforts of enabling more value creation in the #IoT ecosystem gets a boost and encourages us to continue to take our top of the value chain approach.

I look forward to seeing you at #EDASummit17 and having an engaging discussion. Visit www.haveyouAIRed.com for more details/registration.

Best Regards,

Umesh Bhutoria