MANUFACTURING, ENERGY, AND TRANSPORTATION

Make sense of your IoT data

We help leading manufacturers, transportation, and energy companies to improve their physical operations using anomaly detection models and tools.

Use cases

MONITORING

Continuously analyze thousands of signals

IoT sensors generate a large number of signals, which can be challenging to monitor, analyze, and react to. Our platform for Industry 4.0 analytics can monitor thousands of signals continuously, learning normal patterns and detecting anomalous behavior.

detection

Detect anomalies early, prevent propagation

High latency in anomaly detection can result in financial losses, major outages, and liabilities. At the same time, instant anomaly detection is challenging because of noises and outliers that lead to false positives. Our anomaly detection models are designed to optimize the trade-off between detection latency and accuracy using variable time windows and analysis of historical patterns.

detection

Detect cross-metrics patterns

IoT metrics are often collected from complex environments that have multiple interrelated components. In such environments, the analysis of individual metrics can be inefficient because the presence or absence of anomalies in individual signals does not fully characterize the status of the entire environment. Our platform uses topology-aware deep learning models that account for dependencies among sensors and learn complex patterns that involve multiple metrics.

detection

Detect anomalies in images

Anomaly detection in images and videos is one of the most efficient ways to monitor manufacturing and transportation processes, detect defects, and identify security issues. We have extensive experience in computer vision and labeling image data, which helps to develop reliable and efficient anomaly detection solutions.

tools

Investigate issues using advanced tools

Anomaly detection is only part of a complex process that includes issue triaging, root cause analysis, troubleshooting, and feedback-based system tuning. Our anomaly detection models are engineered from the ground up to provide advanced insights that help to investigate issues: anomaly timeframes, severity scores, and correlated metrics. Our solutions also include advanced dashboards for visualizing these insights and performing root cause analysis.

tools

Receive insightful and relevant alerts

Although alerting might appear to be a straightforward task, its practical implementation is associated with some challenges, such as creating insightful summaries that help to investigate the issue and fine-tuning the alerting thresholds and severity levels based on operations team feedback. Our solutions provide features that address these advanced aspects of alert management and tuning.

Scenarios

Robotics

Our platform for Industry 4.0 analytics can be used to collect data from assembly line sensors and detect failures that affect quality, performance, or stability. We use models that account for hardware and sensor topology to reliably differentiate temporary local anomalies from failures that affect the manufacturing process.

Robotics

Our platform for Industry 4.0 analytics can be used to collect data from assembly line sensors and detect failures that affect quality, performance, or stability. We use models that account for hardware and sensor topology to reliably differentiate temporary local anomalies from failures that affect the manufacturing process.

Trаnsportation

Fleet management in the mining, shipping, and trucking industries, as well as in smart city applications, requires continuously collecting and analyzing signals from thousands of vehicles. We have experience building outlier detection and predictive maintenance solutions that help to reduce maintenance costs, trip times, and delays, and improve operations efficiency.

Trаnsportation

Fleet management in the mining, shipping, and trucking industries, as well as in smart city applications, requires continuously collecting and analyzing signals from thousands of vehicles. We have experience building outlier detection and predictive maintenance solutions that help to reduce maintenance costs, trip times, and delays, and improve operations efficiency.

Infrastructure

Failures in the transfer of chemicals and oil are associated with high risks, major losses, and liability costs. We help companies that operate such transfer processes and infrastructures to create anomaly detection solutions and troubleshooting tools that reduce reaction times, costs, and risks.

Infrastructure

Failures in the transfer of chemicals and oil are associated with high risks, major losses, and liability costs. We help companies that operate such transfer processes and infrastructures to create anomaly detection solutions and troubleshooting tools that reduce reaction times, costs, and risks.

How anomaly detection platform for IoT works

Forecasting models

Most anomaly detection techniques are based on the ability to model the monitored process and forecast metric values. To make accurate predictions, we use state-of-the-art deep learning forecasting models that account for metric correlations and the topology of the sensor network.

Inference

The inference process continuously tracks the difference between the forecasted values and the ongoing sensor signals. The forecasting error is analyzed using multiple sliding windows and risk scoring models, the risk scores are thresholded, and alerts are generated.

Anomaly source analysis

In practice, it is not enough to just detect anomalous situations. Our platform for Industry 4.0 analytics includes models and tools that trace the issues down to individual metrics and facilitate root cause analysis.

Our clients

Manufacturing

Finance & Insurance

Hi-tech

Retail

How to get started

We provide flexible engagement options to help you build IoT anomaly detection solutions faster. Contact us today to start with a workshop, discovery, or proof of concept (POC).

Workshops

We offer free half-day workshops with our top experts in data science and anomaly detection techniques to discuss your processes, analytics tools and technologies, and opportunities for improvement.

Proof of concept

If you have already identified a specific use case for anomaly detection, we can usually start with a 4‒8 week proof-of-concept project to deliver improvements and tangible results.

Discovery

If you are in the requirements analysis and strategy development stage, we can start with a 2‒3 week discovery phase to identify the right use cases for anomaly detection, design your solution or product using industry best practices, and build a roadmap.

Learn more

Add anomaly detection to your data with Grid Dynamics Starter Kit

This article describes our real-time cloud-based Anomaly Detection Solution. We cover its design and applicability to the most common use cases: monitoring, root cause analysis, data quality, and intelligent alerting. The solution is AI driven and implements a flexible approach based on extraction of normal state and behavior patterns, but it does not rely on purely statistical methods.