IoT Analytics
Harness the Power of IoT Analytics, turn Massive Amounts of Sensor Data into Actionable Insights
IoT Analytics helps manufacturers leverage the massive amounts of data their connected devices and systems generate to optimize production. However, implementing it can be challenging, especially for manufacturers looking to monitor and diagnose their plants’ physical assets.
Valiance offers a proprietary IoT-based platform that enables remote monitoring and diagnostics of plant assets. We use a combination of sensors and scalable cloud software to provide real-time insights into asset health, plant efficiency, and output. Our comprehensive IoT analytics solution addresses data complexity, lack of expertise, and integration with existing systems, enabling manufacturers to optimize performance, reduce costs, and improve customer experience.
Our Benefits
Improved efficiency
By delivering real-time insights into production processes and helping manufacturers address inefficiencies quickly, IoT analytics can significantly improve plant operations. Its ability to collect and analyze data from connected devices and sensors allows manufacturers to optimize processes, reduce waste, and improve quality control. It also helps manufacturers predict equipment failures before they occur, so they can conduct maintenance proactively, minimize downtime, and streamline operations.
Enhanced decision making
Manufacturers gain a comprehensive understanding of their production processes, supply chain operations, and customer behavior by collecting and analyzing data from connected devices and sensors. As a result, they can identify trends and patterns, optimize processes, and anticipate market demands. IoT Analytics also provides manufacturers with valuable insights into their operations so they can make data-driven decisions, improve their competitiveness, and drive growth.
Improved safety
IoT analytics can boost plant safety by providing real-time monitoring and early warning systems. Monitoring safety equipment, tracking safety compliance, and analyzing safety incident data can alert manufacturers to potential safety risks, helping them prevent accidents. It also enables manufacturers to enhance workplace safety, reduce the risk of accidents, and protect the well-being of their employees.
Reduced costs
IoT analytics helps manufacturers reduce costs by optimizing their processes and reducing waste. It also helps them predict equipment failures before they occur, so they can conduct maintenance proactively, reduce downtime, and take corrective actions to improve efficiency. Ultimately, manufacturers can reduce costs, increase productivity, and enhance their bottom line by leveraging IoT analytics.
Use Cases
Plant Operations Monitoring
Operational Efficiency
Plant Operations Monitoring
Plant operation monitoring with IoT analytics enables predictive maintenance, improves equipment efficiency, optimizes plantoperations, and prevents losses due to theft or inefficiencies. By leveraging predictive maintenance, organizations can reducemaintenance costs and improve the lifespan of their equipment. IoT sensors can be deployed to remotely monitor the performance ofvarious assets, such as vehicles, machines, and tools. This helps improve operational efficiency and prevents theft or losses.Additionally, with instant alerts and status updates provided through remote monitoring, organizations can accurately predict whenmaintenance is needed and schedule regular maintenance to reduce the chances of equipment failure. These preventativemaintenance measures can help optimize operations and reduce costs. Plant Operations
Operational Efficiency
IoT analytics is crucial in improving operational efficiencies by providing real-time insights into data that was previously difficult to access. This allows companies to monitor and optimize their operations to increase productivity, reduce costs, and minimize downtime. For instance, remote monitoring and predictive maintenance can detect faults before they cause costly disruptions in operations and help schedule maintenance and repair work before a breakdown occurs. It also helps with asset tracking, where IoT sensors monitor the movement of assets, such as vehicles and equipment, improving asset utilization, reducing theft, and increasing efficiency
Case Studies
Industrial IoT Implementation For A Global Chemicals Manufacturer
Learn how Valiance helped a Fortune 500 specialty chemicals company achieve operational excellence by implementing a data collection and ingestion pipeline using open-source technologies to remotely monitor industrial assets, improve yield, monitor safety, enable predictive maintenance, and optimize energy
Key Challenges
The primary challenges during the project included extracting data from sensors, filtering out anomalies, and ingesting the data into databases for further monitoring and analysis. The project also required monitoring various physical assets, such as air compressors, cell houses, chlorine compressors, and chlorine liquifiers.
Our Winning Moves
- Valiance created a modular data pipeline using Python libraries. Separate scripts were used for fetching data from sensors, data validation and data ingestion.
- The application was deployed on Azure virtual machines using Fast API. Further, we are using Power BI to display metrics for operational teams.
- TSeparate scripts fetch data from sensors and validate and ingest it. Metadata for sensors, equipment, and other configurations was also maintained.
- Valiance monitored properties such as temperature, pressure, and energy consumption for different physical assets.
- An analytical module determined errors and abnormalities based on expected values and historical data. If deviations were found, alarms were raised and sent to the end user as email, SMS, and app channels notifications.
Outcome
We completed the first phase of digitizing assets by implementing monitoring applications. As a next step, we are building predictive capabilities and dockerizing the applications to make them suitable for multi-cloud hybrid deployments
IoT Based Power Plant Monitoring
Learn how we helped a state-owned Indian hydel power generation company build a cloud-based IoT platform to enable centralized data collection, real-time monitoring, KPI reporting, and advanced AI workloads.
Key Challenges
The client was operating three power plants with generation capacities between 30 MW and 240 MW. These plants have been operational for decades, using equipment from different manufacturers.
The operational team uses electronic hardware deployed at plant sites to monitor plants and assets (transformers, generators, and turbines) and gauge parameters like voltage levels, current readings, pressure levels, fan speed, and vibrations. The plants had localized sensors that did not exchange data with a central location. The client wanted to build a cloud-based IoT platform to enable centralized data collection, real-time monitoring, and advanced AI workloads. In addition, the solution needed to be scalable to accommodate future expansion across multiple power plants.
Our Winning Moves
- Valiance worked with the client's technology team to evaluate cloud vendors and chose AWS as the platform to develop the solution.
- We developed a technology architecture comprising a data lake environment on S3, Kinesis streams for data ingestion, lambda functions for pre-processing sensor data, and Athena queries for reporting.
- We equipped different plant assets with sensors for remote IoT monitoring and integrated 123 sensors across three transformers, generators, transformers, and a single waterconducting system.
- Multi-user and role-based access enable different audiences to view real-time and historical data points. For instance, the operational team could access real-time asset data, historical trends, and patterns.
Outcome
- 1 month go live with the IoT platform across a single power plant
- +2 power plants to enable advanced AI workloads in the future.
- The model was trained to predict the Image tag (animal species) using the generated feature vectors.
Implementing an AI/ML Solution to Improve Cloth Quality for a Pulp and Fibre Manufacturing Company
Our Winning Moves
To overcome the challenges, the company implemented an AI/ML solution using IoT sensors to predict OPU. The solution used the following approach:
- The AI/ML solution, developed in collaboration with the client, was deployed on an EC2 (Elastic Compute Cloud) instance and hosted on a virtual AWS server. This helped the client save time, effort and cost of maintaining physical servers
- Sensors measured the production rate, viscous flow, speed of the machine and its dryer, temperature of multiple dryers, soft finish dozing flows, and circulation.
- IoT data fetched using APIs from Aspen servers in the plant every 10 minutes was fed into the Influx DB (a time series database) maintained on EC2. This data was the basis for predicting the OPU.
- Predictions generated by the AI algorithm using sensor data were stored back into the Influx DB.
- End-users accessed the solution through a Grafana dashboard that displayed OPU predictions at 1-hour intervals to avoid fluctuations.
- Rather than creating a fresh docker image for every new release, the trained models were pushed to a git repository and refreshed in the docker container for ML in production.
Outcome
- AI/ML solution with over 90% accuracy
- Reduced dependency on manual testing and improved cloth quality
- Deployed in two plants in India, with plans for international expansion
- The company plans to adopt AI to enhance other quality metrics
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