Anomaly Detection

Assembly Line

Predicting Defrost in Refrigeration Cases at Walmart using Fourier Transform

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Author: Sujay Madbhavi

Topics: Anomaly Detection, E-commerce

Organizations: Walmart

As the largest grocer in the United States, Walmart has a massive assembly of supermarket refrigeration systems in its stores across the country. Food quality is an essential part of our customer experience and Walmart spends a considerable amount annually on maintenance of its vast portfolio of refrigeration systems. In an effort to improve the overall maintenance practices, we use preventative and proactive maintenance strategies. We at Walmart Global Tech use IoT data and build algorithms to study and proactively detect anomalous events in refrigeration systems at Walmart.

Read more at Walmart Global Tech

Condition monitoring in steel mills: 3 fault detections

Forecast Anomalies in Refrigeration with PySpark & Sensor-data

Date:

Topics: anomaly detection, predictive maintenance, cloud computing

Organizations: Walmart

A refrigeration has four important components: Compressor, Condenser Fan, Evaporator Fan & Expansion Valve. Loosely speaking, together they try to keep the pressure at a reasonable level so as to maintain the temperature within (Remember, PV = nRT). In Walmart, we collect sensor data for all of these components (eg. pressure, fan speed, temperature) at a 10 minutes interval along with metrics like if the system is in defrost or not, compressor is locked out or not etc. We also capture outside air temperature as it impacts the condenser fan speed and in turn, the temperature.

The objective is to minimize the number of malfunctions and suggest probable resolutions of the same to save time. So, we leveraged this telemetry information in order to forecast anomalies in temperature, which would help in prioritizing issues and be proactive rather than reactive.

Read more at Walmart Global Tech Blog

Intelligent edge management: why AI and ML are key players

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Authors: Fetahi Wuhib, Mbarka Soualhia, Carla Mouradian, Wubin Li

Topics: AI, machine learning, edge computing, anomaly detection

Organizations: Ericsson

What will the future of network edge management look like? We explain how artificial intelligence and machine learning technologies are crucial for intelligent edge computing and the management of future-proof networks. What’s required, and what are the building blocks needed to make it happen?

Read more at Ericsson

Using Machine Learning to identify operational modes in rotating equipment

Date:

Author: Frederik Wartenberg

Topics: anomaly detection, vibration analysis, machine learning

Organizations: Viking Analytics

Vibration monitoring is key to performing condition monitoring-based maintenance in rotating equipment such as engines, compressors, turbines, pumps, generators, blowers, and gearboxes. However, periodic route-based vibration monitoring programs are not enough to prevent breakdowns, as they normally offer a narrower view of the machines’ conditions.

Adding Machine Learning algorithms to this process makes it scalable, as it allows the analysis of historic data from equipment. One of the benefits is being able to identify operational modes and help maintenance teams to understand if the machine is operating in normal or abnormal conditions.

Read more at Viking Analytics Blog