ANOMALY DETECTION
INTELLIGENT SYSTEM MONITORING
INTELLIGENT SYSTEM MONITORING
30% faster problem detection
With an advanced anomaly detection system, you can accelerate problem identification by 30%, enabling faster corrective actions and minimizing losses.
4 times greater precision
Anomaly detection algorithms provide four times greater precision in identifying deviations from the norm, resulting in more accurate diagnoses and improved production quality.
Elimination of costly failures
Anomaly detection systems eliminate the risk of unexpected failures, allowing for preventive maintenance actions, which significantly reduces costs associated with downtime and repairs.
Let’s take a look at the supply chain in manufacturing. The number of production machines, sensors, and parameters, as well as the volume of data generated, can no longer be effectively analyzed by humans alone. Our intelligent algorithms process this data and translate it into more meaningful insights, enabling anomaly prediction and production optimization. This allows you to detect anomalies before products reach quality control, reduce unplanned downtime, and prevent costly failures. Our solutions provide decision-makers with unprecedented insights, enabling more informed decisions.
Anomaly Detection in Machine Maintenance
In machine maintenance, anomaly detection involves predicting abnormalities and optimizing processes in advance, rather than incurring losses. The goal of anomaly detection is to identify patterns that deviate from the rest of the data, known as anomalies. These could be events or faulty components, primarily resulting from equipment failures during the production process. A defective production process—marked by quality control errors due to unnoticed gradual loss of machine calibration—plays a significant role in disrupting the supply chain and causing sales and market losses.
Managing the Anomaly Detection Process
However, this process is manageable. There is a wealth of data suitable for anomaly detection: various parameters such as temperature, vibrations, pressure, humidity, chemical reactions, spectroscopy, and more. Our company specializes in measuring data from machines and control stations.
Automating the Anomaly Detection Process
What if we told you that you could automatically detect defective products, significantly reduce unplanned downtime, prevent costly failures, and extend the lifespan of aging assets? When traditional solutions fail, it’s time to implement intelligent software like machine learning to analyze data and uncover non-obvious relationships. This allows for anomaly prediction, helping to reduce costs and build a robust organization.
Anomaly Detection and Process Optimization in Industry 4.0
In the manufacturing process, many things can go wrong. Sometimes your team can quickly identify and fix them. But what if intuition and experience are not enough to solve all the problems? How do you manage if you need data that you don’t even know where to collect or how to analyze?
The questions are: What can you do to address serious issues, and how can you find subtle relationships even if they are not visible to the naked eye? The number of machines, parameters, and the volume of data generated can no longer be effectively analyzed by humans alone. Intelligent algorithms are the way to predict anomalies and optimize production.
Anomaly Detection in Machine Maintenance
In machine maintenance, anomaly detection involves predicting abnormalities and optimizing processes in advance, rather than incurring losses. The goal of anomaly detection is to identify patterns that deviate from the rest of the data, known as anomalies. These could be events or faulty components, primarily resulting from equipment failures during the production process. A defective production process—marked by quality control errors due to unnoticed gradual loss of machine calibration—plays a significant role in disrupting the supply chain and causing sales and market losses.
Managing the Anomaly Detection Process
However, this process is manageable. There is a wealth of data suitable for anomaly detection: various parameters such as temperature, vibrations, pressure, humidity, chemical reactions, spectroscopy, and more. Our company specializes in measuring data from machines and control stations.
Automating the Anomaly Detection Process
What if we told you that you could automatically detect defective products, significantly reduce unplanned downtime, prevent costly failures, and extend the lifespan of aging assets? When traditional solutions fail, it’s time to implement intelligent software like machine learning to analyze data and uncover non-obvious relationships. This allows for anomaly prediction, helping to reduce costs and build a robust organization.
Anomaly Detection and Process Optimization in Industry 4.0
In the manufacturing process, many things can go wrong. Sometimes your team can quickly identify and fix them. But what if intuition and experience are not enough to solve all the problems? How do you manage if you need data that you don’t even know where to collect or how to analyze?
The questions are: What can you do to address serious issues, and how can you find subtle relationships even if they are not visible to the naked eye? The number of machines, parameters, and the volume of data generated can no longer be effectively analyzed by humans alone. Intelligent algorithms are the way to predict anomalies and optimize production.
Let’s take a closer look: Example Use Case
Predicting Production Anomalies
Industry: Automotive, preliminary assembly (international company)
Objective: Reduce the number of defective parts and detect anomalies in machines
Solution: Exploratory Data Analysis
Effective analysis of machine and system data, supported by machine learning algorithms, enables the development of models that identify less obvious factors contributing to production losses.
The first step was to ask the right questions. Among various production processes, we selected the one most prone to anomalies and critical to the company. We thoroughly analyzed this process from start to finish, identifying areas that required closer examination using machine learning to uncover less apparent dependencies affecting losses. We then processed the data and conducted exploratory analysis using various visualizations.
What is a Prototype?
Rapid prototyping allows for quick validation of assumptions and focuses on areas with the greatest business potential. A high-precision machine learning-based prototype highlighted key nonlinear correlations responsible for anomalies. This enabled us to uncover previously invisible dependencies, leading to more accurate decision-making.
What is a Production Model?
After creating the prototype, we identified the factors with the most significant impact on achieving the objective. We then built a robust machine learning model that operates throughout the entire production cycle. This model helps detect factors causing anomalies at an early stage, allowing for the implementation of necessary preventive measures.
Sustainable Production Process:
Ensuring the stability and efficiency of the production process through the appropriate implementation of data science and artificial intelligence.
Application of Machine Learning in the Supply Chain
Machine learning has broad applications across various areas. In the supply chain, the most common uses include: demand forecasting based on AI (considering location, category, brand, store, and SKU), return forecasting, minimizing stockouts, forecasting new products, and price optimization.
Predicting Production Anomalies
Industry: Automotive, preliminary assembly (international company)
Objective: Reduce the number of defective parts and detect anomalies in machines
Solution: Exploratory Data Analysis
Effective analysis of machine and system data, supported by machine learning algorithms, enables the development of models that identify less obvious factors contributing to production losses.
The first step was to ask the right questions. Among various production processes, we selected the one most prone to anomalies and critical to the company. We thoroughly analyzed this process from start to finish, identifying areas that required closer examination using machine learning to uncover less apparent dependencies affecting losses. We then processed the data and conducted exploratory analysis using various visualizations.
What is a Prototype?
Rapid prototyping allows for quick validation of assumptions and focuses on areas with the greatest business potential. A high-precision machine learning-based prototype highlighted key nonlinear correlations responsible for anomalies. This enabled us to uncover previously invisible dependencies, leading to more accurate decision-making.
What is a Production Model?
After creating the prototype, we identified the factors with the most significant impact on achieving the objective. We then built a robust machine learning model that operates throughout the entire production cycle. This model helps detect factors causing anomalies at an early stage, allowing for the implementation of necessary preventive measures.
Sustainable Production Process:
Ensuring the stability and efficiency of the production process through the appropriate implementation of data science and artificial intelligence.
Application of Machine Learning in the Supply Chain
Machine learning has broad applications across various areas. In the supply chain, the most common uses include: demand forecasting based on AI (considering location, category, brand, store, and SKU), return forecasting, minimizing stockouts, forecasting new products, and price optimization.
Benefits of Implementing Anomaly Detection Technology
Key Benefits of Implementing Anomaly Detection Systems in Manufacturing
Reduction of Defective ProductsSignificant reduction in the number of defective parts, leading to lower production losses.
Early detection of anomaliesDetecting issues at an early stage, which helps save production time.
Increased operational efficiencyAutomating anomaly detection processes allows for resource optimization, minimizing downtime and increasing production efficiency.
Reduction in maintenance costsEarly identification of technical issues enables scheduled maintenance, helping to avoid costly failures and extend the lifespan of machinery.
