Industrial Automation Magazine India

Today advanced maintenance software can calculate the health of the asset

Published on : Friday 07-10-2022

Sijesh Manohar, Vice President, Product Development, SAP Labs LLC, Palo Alto. 

Condition monitoring is commonly understood to apply to rotating machinery. What other assets in the plant can benefit from condition monitoring?
Condition monitoring can be applied to any kind of asset that is operational and requires maintenance. The sensors or telemetry data from the asset is analysed to predict the probability of downtime for any asset. Today advanced maintenance software can calculate the health of the asset based on its telemetry data to determine the most optimal maintenance schedule. This method as opposed to the normal periodic maintenance reduces the cost of maintenance significantly and avoids downtimes more deterministically. In addition to this use case, the term condition monitoring is also used to monitor the telemetry data from containers or vehicles that are used for carrying perishable items such as medicines and food. An example of such telemetry data is temperature and humidity which are often monitored in the transportation of perishable goods.
 
What is an estimate of the size of the market?
The biggest use case for condition monitoring is predictive maintenance. The predictive maintenance market saw an estimated revenue generation of approximately USD 5 billion in 2021 and is estimated to grow at a CAGR of 26-27% to reach around USD 20 bn by 2027. With these estimates, by 2030 it could reach around USD 45 bn.
 
Condition monitoring and prescriptive analytics is a business activity different from legacy sales and services activities. Do you think MSMEs and startups have an inherent advantage in getting market share?
Condition monitoring can be seen as core to the business as it reduces cost and brings efficiency. How is it core to the business? Take for example a manufacturer wanting to provide excellent after-sales service for their products (such as industrial machines or consumer appliances). They could guarantee 24x7 operation of the machine for a small maintenance fee, assuming their customer agrees to provide real-time access to usage telemetry data of the machine. Manufacturers and 3rd party service maintenance providers of such products can provide proactive service by enabling their field service technicians an accurate analysis of potential or reported problems remotely. Remote analysis of usage data can allow new business models such as ‘pay per use’ that allows small and medium enterprises to ‘lease’ such assets and get billed for the usage and uptime, rather than making an upfront purchase.
 
Are there real studies done to establish proof of concept using simulation and digital twin techniques? Is the appetite for such systems bigger at large plant operators like power plants and refineries?
The sensor data collected from assets is traditionally used in two different ways. One way is to apply machine learning techniques such as prediction and anomaly detection algorithms to calculate health indicators and predict failures. The second method is to apply engineering simulation to the digital twin representation of the asset. In this method, the data collected from physical sensors are used to extrapolate virtual sensor readings on other parts of the asset using digital simulation. This is useful in assets where it is highly expensive to place sensors on parts of large physical assets (e.g., large wind turbines) or practically impossible to place sensors (e.g., a deep sea drilling machine). Using the digital twin representation and data collected from physical sensors on accessible parts of the assets like the top of the drill and applying engineering simulations to calculate virtual sensor readings on the drill buried deep beneath the earth. Using the simulated values, the stress and strain on those inaccessible parts of the asset can be calculated using mathematical formulas to determine the possibilities of failure. There are real-world examples where this simulation technique is being used – for example in Scandinavian countries certain roadway bridges, digital twin representation, and simulation techniques are used to calculate stress and strain on different parts of the bridge to avoid failures.
 
Sijesh Manohar is a Vice President in IoT Product Development at SAP Labs LLC Palo Alto, California – USA. Sijesh has successfully led global development teams in India and US for several innovative software products at SAP. Recently he has been extensively working in the research and development of new IoT and Edge products. He is the co-author of the book "Internet of Things with SAP" - SAP PRESS 2020.
https://www.linkedin.com/in/sijesh-manohar-b080044/
 
(The views expressed in interviews are personal, not necessarily of the organisations represented)