designing algorithms for industrial iot analytics

Srikanth Muralidhara, Flutura Decision Sciences and Analytics

October 2015

Algorithms &

Industrial IOT

Forces at Play - The New World Order

• Increased focus on Super Optimization and Automation in mature markets

• Innovation will be led from emerging markets and then brought to mature markets

Source : Oxford Economics

2021 G8

Compounded by Scale of Inefficiencies

Electric power transmission and distribution losses (% of output) in India was 27%

Global retail industry inventory preventable loss at $300 billion

And many more…..

Wide range of unsolved problems – Need for Innovative SolutionsA core part of the solution would be Scalable Algorithms

Industrial IOT Areas and Applicability of Algorithms

4Source : Industrial Internet Consortium

Think “Narrow + Deep”

5Formulate Problems - Narrow and Deep

What should be the tilt in the runner to

maximize RPM?

When can the first service be deferred for a

given seismic region ?

How soon would the pump be affected due to

cavitation?

Think “Subsytems”

6Define Sub System Boundaries and Interaction Points Clearly

Think “Breadth+ Depth”

7Richness in the breadth and optimal depth in data. This is where it all starts !

8

Grey-Box Model

Gracefully marry mechanistic and statistical models

𝑃 = 𝑃𝑜𝑤𝑒𝑟𝜂 = 𝑡𝑢𝑟𝑏𝑖𝑛𝑒 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦𝜌 = 𝑑𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝑤𝑎𝑡𝑒𝑟

𝑔 = 𝑔𝑟𝑎𝑣𝑖𝑡𝑎𝑡𝑖𝑜𝑛𝑎𝑙 𝑐𝑜𝑛𝑠𝑡𝑎𝑛𝑡ℎ = ℎ𝑒𝑖𝑔ℎ𝑡 𝑞 = 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒

Think “Greybox”

Think “Edge + Central ”

9

Location of the algorithm - Edge versus Centrally Controlled Intelligence

Think “Autonomous”

10Extent of Automation and Control

Unmanned systemsRemotely Assisted Remotely Controlled

Tying it all together – Predicting Turbine Failure

Data Preprocessing

•Mahalonobis Outlier Detection

•Pearson Correlation

•Missing Value Imputation

Feature Engineering

•Dominant Frequency Analysis

•Principal Component Analysis/Factor Analysis

•Mean Time Between Failure

Model Development

• Survival Analysis

• Logistic Regression

•Boosted Trees

•Random Forest

• What is the probability the coupler will survive for the next 6 months ?

• Which components are in the last stage of degradation ?

• Is the turbine X axis vibration in the normal operating range?

• Which are the main lead indicators affecting the plant load factor?

• What is the operating range of turbine X axis vibration?

• How does the lubricant oil temperature affect the bearing temperature?

Closing thoughts – 3 core points

• Age of Super Automation taking shape• Intersect of IOT + Analytics can pave the way for responding to this market condition

• Algorithms for unsolved problems

• Grey Box algorithms for better outcomes• Mechanistic + Statistical Models

• Breadth of data key to realizing successful outcomes• Uncovering and fixing data blind spots

13

“The price of light is less than the cost of darkness”Arthur C. Nielsen

IOT

Analytics

srikanth@flutura.com