unlocking the power of Iot
with intelligence

MACHINE INTELLIGENCE

Data has become a valuable resource, and it’s cheaper than ever to capture and store. Through the use of artificial intelligence and Machine Learning, analyzing the data can significantly improve production efficiency, product quality, and employee safety.
At InterNext we provide Machine Learning solutions by deploying process-based models for complex data interpretation, detection of trends and identification of the patterns. With our expertise in cloud computing, we help you to think intelligently and bring you the benefits of ML and AI to ensure better operational efficiency.

ARTIFICIAL INTELLIGENCE OF THINGS

Normally IoT enabled devices are largely deterministic in nature, relying upon autonomic systems making decisions based on predetermined rules that take action based upon the occurrence of specific events. More or less, IoT devices will act as passive devices, deployed to transfer data for further analysis or processing. The task of processing the data and providing meaningful insight is limited to a cloud computing platform in this type of environment.
Artificial Intelligence enabled IoT (AIoT) enables IoT networks and systems to become more cognitive, robust and even more scalable. 
InterNext is encouraging cutting edge AI innovation with its IoT platforms; designing a wide range of IoT devices with Edge-AI capabilities, new user experiences and addressing new market opportunities.

USE CASES


Machine Intelligence
IoT driving the future of Maintenance in Industries

Maintenance represents a significant part of any manufacturing operation’s expenses. To deal with this, Preventive maintenance has emerged as a leading Industry 4.0 use case for manufacturers and asset managers. Implementing industrial IoT technologies to monitor asset health, optimize maintenance schedules, and gaining real-time alerts to operational risks, allows manufacturers to lower service costs, maximize uptime, and improve production throughput.

Challenges
Predictive maintenance for industry is a method of preventing failures in operations, by analyzing production data to identify patterns and predict difficulties. Until now, factory operators carried out scheduled maintenance and regularly repaired machine parts to prevent downtime, hence, consuming unnecessary resources and driving productivity losses.

Solutions
Data sets, obtained from the ongoing factory operations, can be used to train machine learning algorithms and detect anomalies while searching for patterns across the various data feeds. This way machine learning enables predictive monitoring, forecasting equipment breakdowns before they occur and scheduling timely maintenance.

Benefits
Predictive analytics algorithms when applied to the feed data, recognize patterns and generate insights in the form of dashboards and alerts, this minimizes the risk of unpredicted failures and reduces the amount of unnecessary preventive maintenance activities required enable root cause analysis of a system to uncover issues ahead of failure.


Machine Intelligence

Use AIoT to make your factory safe

Condition monitoring is increasingly being used by factory operators as a way to reduce downtime, keep at peak operating efficiencies, and lower equipment costs. It brings big benefits including streamlined off-site troubleshooting.

Check out Laxmi Machine Works improved its Production Efficiency using Internext's Solution

Challenges
Condition monitoring for industries is a method of preventing failures in operations. The industry needs to provide a quick response to deviations from Standard Operating Conditions to minimize the revenue loss and ensure effective use of skilled manpower. Until now, factory operators carried out scheduled maintenance and regularly repaired machine parts to prevent downtime, hence, consuming unnecessary resources and driving productivity losses.

Solution
InterNext uses Artificial intelligence along with Internet of things (AIoT), to provide real-time monitoring of the environmental conditions of manufacturing operations. A lot of data is generated every millisecond so it can’t be directly passed to cloud, therefore, Machine learning models were incorporated to reveal hidden correlations in data sets and detect abnormal data patterns at the edge. Once the data is prepared it can be transferred using RS232 to LoRaWAN converter over LoRa Network.

Benefits
Condition Monitoring provides real-time insights into the equipment condition allowing teams to attend to the issues in time. This helps in avoiding machinery damage and optimizing machine downtime occurrences and start maintaining the machines before they break down.


Machine Intelligence
IoT driving the future of Maintenance in Industries

Maintenance represents a significant part of any manufacturing operation’s expenses. To deal with this, Preventive maintenance has emerged as a leading Industry 4.0 use case for manufacturers and asset managers. Implementing industrial IoT technologies to monitor asset health, optimize maintenance schedules, and gaining real-time alerts to operational risks, allows manufacturers to lower service costs, maximize uptime, and improve production throughput.

Challenges
Predictive maintenance for industry is a method of preventing failures in operations, by analyzing production data to identify patterns and predict difficulties. Until now, factory operators carried out scheduled maintenance and regularly repaired machine parts to prevent downtime, hence, consuming unnecessary resources and driving productivity losses.

Solutions
Data sets, obtained from the ongoing factory operations, can be used to train machine learning algorithms and detect anomalies while searching for patterns across the various data feeds. This way machine learning enables predictive monitoring, forecasting equipment breakdowns before they occur and scheduling timely maintenance.

Benefits
Predictive analytics algorithms when applied to the feed data, recognize patterns and generate insights in the form of dashboards and alerts, this minimizes the risk of unpredicted failures and reduces the amount of unnecessary preventive maintenance activities required enable root cause analysis of a system to uncover issues ahead of failure.


Machine Intelligence

Use AIoT to make your factory safe

Condition monitoring is increasingly being used by factory operators as a way to reduce downtime, keep at peak operating efficiencies, and lower equipment costs. It brings big benefits including streamlined off-site troubleshooting.

Check out Laxmi Machine Works improved its Production Efficiency using Internext’s Solution

Challenges
Condition monitoring for industries is a method of preventing failures in operations. The industry needs to provide a quick response to deviations from Standard Operating Conditions to minimize the revenue loss and ensure effective use of skilled manpower. Until now, factory operators carried out scheduled maintenance and regularly repaired machine parts to prevent downtime, hence, consuming unnecessary resources and driving productivity losses.

Solution
InterNext uses Artificial intelligence along with Internet of things (AIoT), to provide real-time monitoring of the environmental conditions of manufacturing operations. A lot of data is generated every millisecond so it can’t be directly passed to cloud, therefore, Machine learning models were incorporated to reveal hidden correlations in data sets and detect abnormal data patterns at the edge. Once the data is prepared it can be transferred using RS232 to LoRaWAN converter over LoRa Network.

Benefits
Condition Monitoring provides real-time insights into the equipment condition allowing teams to attend to the issues in time. This helps in avoiding machinery damage and optimizing machine downtime occurrences and start maintaining the machines before they break down.