Empowering Industrial Intelligence with AI and ML
The opportunities presented by Artificial Intelligence (AI) and Machine Learning (ML) in embedded software, edge devices, and sensors are vast and transformative. With AI and ML, businesses can make better decisions without human intervention by using algorithms to build models that uncover connections, paving the way for a new era of industrial automation.
We at Unikie harness the power of AI and ML to deliver real-time industrial systems with exceptional performance, lower cloud cost and lower hardware requirements. AI and ML are finding applications across a wide range of industrial domains, including:
- Predictive Maintenance
- Real-time Process Control & Optimization
- Anomaly Detection and Fault Diagnosis
- Quality Monitoring & Forecasting
- Predictive Demand Forecasting
Partnering for Industrial Transformation
Our approach to AI and ML integration in industrial systems is deeply rooted in understanding the unique challenges and opportunities of the industrial environment. We carefully tailor our solutions to address the specific needs of each industry, ensuring seamless integration with existing infrastructure and processes.
AI & ML in Embedded Software
AI & ML in Edge devices
AI & ML in Sensors
Path to AI driven processes
AI and ML-powered real-time industrial systems are the cornerstone of Industry 4.0, enabling predictive maintenance, autonomous control, and flexible manufacturing to name a few. We are committed to providing our clients with the tools and technologies they need to transform their industrial operations, achieving new levels of efficiency, productivity, and safety. See some of our projects:
Collecting data with a raw material processing QA system
Collecting data
Resilient way of acquiring, transferring, synchronizing and preprocessing data is a mandatory prerequisite for all real-world intelligent solutions. In industrial setting they come in various shapes and forms.
Data can be generated by both humans and machines and can vary from distributed, low level sensor data all the way to the records from centralized company level information systems. It is often a non-trivial and tedious task and hence a typical reason for failing in the solution deployment and operation.
Example
We provided a forestry company with an automatic raw material processing QA system that monitors the material quality in production visually, using AI based and classical machine vision algorithms. The solution can identify raw material features to qualify for production process, identify quality problems on different production phases, and perform quality check on the final product.
We analyzed the production process and proposed the overall solution, starting from sensor and hardware selection and placement for optimal operation, all the way to the machine vision software implementation, testing, integration, and deployment.
Turning data into knowledge with realtime quality dashboard
Building data pipelines
Data as such are just a raw material with little value. Just like any raw material, they need to be refined and distributed to result in something useful. This can be achieved by building efficient data pipelines that carry out various data processing steps, often in a distributed manner in realtime.
A typical data pipeline includes ingesting and merging data from different sources, cleaning and transforming them, applying algorithms and visualization methods on datasets, and finally transferring the results in timely and in right format to the one’s in need of them.
Example
We built an international manufacturing company an AI-based solution that provides information on the end product quality to the machine operators through a realtime dashboard. In addition to overall view of diverse quality measures, the solution shows trends, forecast of quality, as well as displays the key product parameters affecting quality.
The solution was based on combining machine vision sensor data with production data, and analyzing them using diverse visualization approaches, as well as applying machine learning methods for anomaly detection, prediction and forecasting.
Drawing conclusions with predictive maintenance
Conclusions
To make the gained knowledge actionable, inferences need to be made based on them to draw concrete conclusions. This is often a demanding task, even for a seasoned domain area expert, but still in many cases inferences can be made by advanced AI algorithms with little or no human interaction.
Although fully automatic decision processes are something to desire for and increasing in number, usually at least the final conclusions are drawn by a human. In some cases that is even mandatory for regulative and/or security reasons.
Example
We helped an international mining company to build their predictive maintenance capabilities and products. The focus was in optimizing the methodologies and improving forecasting skills. Our experts have both ensured the data quality for its particular use, but also built predictive machine learning models and needed KPIs.
This has been achieved by robust understanding about the needs and business. In practice the activities included building ML models for individual failure mechanisms, identifying data flaws that compromise the integrity of the results, and optimizing the overall data-to-value processes.
Taking actions with AI/ML powered operator assistant
Decision making
The last mile of the AI driven processes is taking the action. The final decisions are made based on the conclusions, which lead to either manual or automatic action. Like drawing conclusions, aim in the long run is to automate action-taking as much as possible.
However, in industrial setting there usually is a human in the loop, to either actively carry out the procedure recommended by the AI, or at least to approve its execution. Closing the loop in a proper manner enables also self-learning systems that stabilize and improve over time.
Example
We developed an AI-based solution for an international manufacturing company for optimized quality control. A realtime dashboard provides the machine operators suggestions of possible process parameters, as well as other relevant information, before a manufacturing of a new batch is started.
Operators can then adjust the process parameters accordingly to produce optimal quality regardless of their experience level. The solution is based on a data and complex ML-based digital twin of the production line that is used for predicting quality and generating recommendations on optimal machine setting.
Unleashing the Power of AI & ML
We are a trusted partner in your industrial transformation journey. Our team of experienced engineers and AI/ML experts work closely with our clients to understand their unique needs and develop customized solutions that deliver tangible results. With our deep expertise in AI and ML, embedded software, and industrial automation, we are committed to empowering businesses to harness the power of intelligent systems and unlock new possibilities for industrial excellence.
