Features

 system health monitoring

System health monitoring

The starter kit provides a reference implementation for system health monitoring use cases. The solution is designed to reliably evaluate system health and detect anomalies in a large number of IoT metrics received from multiple sensors and machines.

Multiple system types

The starter kit includes a design guide that helps determine the right model type based on the properties of the monitored system and availability of the labeled data.

multiple model types
system health

Power of Vertex AI AutoML

The starter kit leverages native Vertex AI AutoML components to evaluate system health. AutoML sharply reduces the feature and model engineering efforts, enabling developers to focus on more business-oriented problems.

Open source

We use only native Google Cloud services and open source libraries, so the starter kit can be easily extended and customized.

native Google Cloud services

Use cases

The starter kit provides out of the box system health monitoring and anomaly detection pipelines. These reference pipelines can be used as a starting point for implementing a broader range of use cases related to Industrial IoT and Smart Manufacturing.
  • defect detectionDefect detection
  • anomaly classificationAnomaly classification
  • remaining useful life estimationRemaining useful life estimation
  • predictive maintenancePredictive maintenance

Industries

System health monitoring and anomaly detection capabilities are important in many industries. The starter kit includes several templates that cover some of the most relevant ones.
discrete manufacturing
Discrete manufacturing

Monitor equipment health and prevent downtimes.

process manufacturing
Continuous manufacturing

Continuosly control the product quality and detect anomalies.

energy industry
Energy

Get the most value from your smart grid investments.

oil and gas industry
Oil and gas

Implement sensor-based tank monitoring and asset monitoring.

 transportation industry
Transportation

Monitor electric vehicles and optimize EV charging.

life science industry
Life sciences

Monitor production processes, ensure quality and compliance.

Why develop a pricing optimization solution in Google Cloud?

How it works

The starter kit includes health scoring models and detection algorithms that make binary decisions and generate alerts.
health scoring models and detection algorithms

Learn more

Anomaly detection in industrial IoT data using Google Vertex AI: A reference notebook
Anomaly detection in industrial IoT data using Google Vertex AI: A reference notebook
In this blog post, we focus on IoT data analysis challenges associated with system health monitoring and how to resolve them.
Read more
Anomaly detection in industrial applications: solution design methodology
In this article, we describe a solution design methodology for anomaly detection based on the labeled data types and availability. We delineate the general strategies for three types of data labeling here and point to some hidden pitfalls that frequently pass unnoticed and may result in productivity disruptions and even project failures.
Read more
Detecting anomalies in high-dimensional IoT data using hierarchical decomposition and one-class learning
We show how anomaly detection solutions for high-dimensional IoT data streams can be implemented using hierarchical decomposition and one-class learning.
Read more

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