LotusEye: AI Anomaly Detection for Sensor Data Analysis

LotusEye

3.5 | 72 | 0
Type:
Website
Last Updated:
2025/10/15
Description:
LotusEye offers AI anomaly detection for sensor data, automatically learning normal behavior and alerting to anomalies. Easy to use with free model creation.
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anomaly detection
sensor data
predictive maintenance
industrial IoT

Overview of LotusEye

LotusEye: AI-Powered Anomaly Detection for Sensor Data

What is LotusEye?

LotusEye is an AI-driven anomaly detection platform designed to automatically learn normal behavior from diverse sensor data and promptly alert users to any signs of anomalies. It simplifies the process of creating AI models by allowing users to upload their data without requiring any prior knowledge of AI.

How does LotusEye work?

LotusEye's AI model analyzes uploaded sensor data to establish a baseline of normal operational behavior. Once trained, the model continuously monitors incoming sensor data for deviations from this baseline. When the AI detects anomalies, it calculates an anomaly score. If this score exceeds a predefined threshold, LotusEye triggers an alert, prompting further inspection.

How to use LotusEye?

Getting started with LotusEye is straightforward:

  1. Upload Training Data: Provide historical data representing normal operations to train the AI model.
  2. Upload Test Data: Submit new data to be analyzed for anomalies.
  3. Review Anomaly Scores: Examine the AI-calculated anomaly scores. High scores indicate potential anomalies that warrant investigation.

Key Features and Benefits:

  • Ease of Use: LotusEye is designed to be user-friendly, requiring no AI expertise.
  • Free Model Creation: Users can create and test AI models without any initial cost.
  • Comprehensive Feature Set: Paid plans offer additional features like email notifications, API data uploads, and multi-member management.
  • Versatile Data Support: LotusEye supports both wide-format and long-format CSV files, accommodating various sensor data structures. Specifically, it supports:
    • Wide-format CSV files: Each row represents sensor values at each timestamp.
    • Long-format CSV files: Contain three columns: "Timestamp," "Sensor Name," and "Sensor Value."

Who is LotusEye for?

LotusEye is suitable for a range of industries and applications, including:

  • Industrial IoT: Monitoring equipment and machinery for predictive maintenance.
  • Manufacturing: Detecting anomalies in production processes.
  • Energy: Analyzing sensor data from power grids and renewable energy systems.
  • Transportation: Monitoring vehicle performance and infrastructure health.

Pricing Plans:

LotusEye offers several pricing tiers to accommodate different user needs:

  • Free: Ideal for initial testing, limited to one model, manual data uploads, and basic feature usage.
  • Light: $14/month (approx.), includes web API data uploads, email alerts, and support for up to 3 management members.
  • Standard: $34/month (approx.), increases sensor data limits, removes upload limits, extends data storage, and supports up to 10 management members.
  • Business: $54/month (approx.), offers the highest data limits, unlimited management members, weekly summary emails, and anomaly analysis reports.

Note: Prices are billed in Japanese Yen and may vary slightly due to exchange rate fluctuations.

FAQ Highlights:

  • LotusEye supports both wide and long format CSV files for sensor data uploads.
  • Anomaly scores are calculated based on deviations from the trained model's understanding of normal behavior. High scores suggest potential anomalies.

Why Choose LotusEye?

LotusEye empowers organizations to proactively detect and address anomalies in their sensor data, minimizing downtime, optimizing performance, and reducing costs. Its user-friendly interface, flexible pricing plans, and comprehensive feature set make it a valuable tool for businesses seeking to leverage AI for anomaly detection.

By using AI to automate anomaly detection, LotusEye enables businesses to shift from reactive to proactive maintenance, improving operational efficiency and overall profitability.

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