Schlagwort: Anomaly Detection

  • Ensure DC motor performance with anomaly detection based on energy monitoring

    Ensure DC motor performance with anomaly detection based on energy monitoring

    Reading Time: 5 minutes
    [youtube https://www.youtube.com/watch?v=jskIyHzcH7A?feature=oembed&w=500&h=281]

    The challenge

    Optimizing manufacturing processes is a requirement in any industry today, with electricity consumption in particular representing a major concern due to increased costs and instability. Analyzing energy use has therefore become a widespread need – and one that can also lead to early identification of anomalies and predictive maintenance: two important activities to put in place in order to minimize unexpected downtime and repair costs. 

    In particular, this approach can be applied to DC motors: used in a wide range of applications, from small household appliances to heavy industrial equipment; these motors are critical components that require regular maintenance to ensure optimal performance and longevity. Unfortunately, traditional maintenance practices based on fixed schedules or reactive repairs can be time-consuming, expensive, and unreliable. This is where energy monitoring-based anomaly detection comes in: it can provide a crucial solution for the early detection of potential issues and malfunctions before they can cause significant damage to the motor. 

    This more proactive approach to maintenance continuously monitors the energy consumption of the motor and analyzes the data to identify any deviations from normal operating conditions. By tracking energy usage patterns over time, the system can detect early warning signs of potential problems, such as excessive wear and tear, imbalances or misalignments, and alert maintenance personnel to take corrective actions before the issue escalates.

    Our solution

    This Arduino-powered solution implements an energy monitoring-based anomaly detection system using a current sensor and machine learning models running on edge devices. By capturing the electricity flowing in and out of a machine, it can collect large amounts of data on energy usage patterns over time. This data is then used to train a machine learning model capable of identifying anomalies in energy consumption behaviors and alerting operators to potential issues. The solution offers a cost-effective and scalable method for maintaining equipment health and maximizing energy efficiency, while also reducing downtime and maintenance costs.

    Motor Current Signature Analysis (MCSA)

    In this application, a technique called Motor Current Signature Analysis is used. MCSA involves monitoring the electrical signature of the motor’s current overtime to detect any anomalies that may indicate potential issues or faults. To acquire real-time data, a Hall effect current sensor is attached in series with the supply line of the DC motor. The data are then analyzed using machine learning algorithms to identify patterns and trends that might indicate a faulty motor operation. MCSA can be used to detect a number of issues like bearings wear, rotor bar bendings or even inter-turn short circuits.

    Depending on the dimensions of the motor, using a non-invasive clamp-style current sensor – also known as a Split-Core Current Transformer – is recommended if a larger current draw is expected.

    Edge ML

    To monitor the current fluctuation and run the anomaly-detecting ML model, the solution uses an Arduino Opta WiFi: a micro PLC suitable for Industrial IoT, which is excellent for this project because of its real-time data classification capabilities, based on a powerful STM32H747XI dual-core Cortex®-M7 +M4 MCU. The Arduino Opta WiFi works with both analog and digital inputs and outputs, allowing it to interact with a multitude of sensors and actuators. The Arduino Opta WiFi also features an Ethernet port, an RS485 half duplex connectivity interface and WiFi/Bluetooth® Low Energy connectivity, which makes it ideal for industrial retrofitting applications. You can find the full datasheet here. 

    To train the anomaly detection model, the project leverages the Edge Impulse platform: being integrated within the Arduino ecosystem, it makes it easy to develop, train, and deploy machine learning models on Arduino devices.

    Connectivity

    Once the machine learning model was successfully deployed on the Arduino Opta, the anomaly detection results were forwarded via WiFi to the Arduino IoT Cloud. This enables easy monitoring and analysis of the data from multiple sensor nodes in real time.

    Solving it with Arduino Pro

    Let’s take a look at how we can put all of this together and what hardware and software solutions we would need for deployment. The Arduino Pro ecosystem is the most recent version of Arduino solutions, offering users the benefits of easy integration along with a range of scalable, secure, and professionally supported services.

    Hardware requirements

    Software requirements

    The Arduino IDE 2.0 was used to program the Arduino Opta WiFi using C/C++. To train the Edge Impulse model, data was gathered from the current sensor for two classes: Normal Operation and Machine Off. The Motor Current Signature Analysis (MCSA) technique was implemented by extracting the frequency and power characteristics of the signal through a Spectral Analysis block. Additionally, an anomaly detection block was incorporated to identify any abnormal patterns in the data.

    Here is a screenshot from a dashboard created directly in the Arduino Cloud, showcasing data received from the sensor nodes:

    Here is an overview of the software stack and how a minimum deployment with one of each hardware module communicates to fulfil the proposed solution:

    Conclusion

    Through the implementation of a predictive maintenance system on an Arduino Opta WiFi PLC, using Edge Impulse ML models and the Arduino Cloud, this solution demonstrates the powerful potential of IoT technologies in industrial applications. With the use of current sensors and AI-driven anomaly detection models, the system enables real-time monitoring and fault detection of DC motors, providing valuable insights for predictive maintenance. The flexibility and scalability of the Arduino Opta WiFi platform make it a robust and cost-effective solution for implementing predictive maintenance systems in various industrial processes. Overall, the project highlights the significant advantages that MCSA and machine learning can offer in promoting efficiency, productivity, and cost savings for industrial processes.

    The post Ensure DC motor performance with anomaly detection based on energy monitoring appeared first on Arduino Blog.

    Website: LINK

  • Predicting potential motor failures just using sound

    Predicting potential motor failures just using sound

    Reading Time: 2 minutes

    Nearly every manufacturer uses a machine at some point in their process, and each of those machines is almost guaranteed to contain at least one motor. In order to maintain uptime and efficiency, these motors must always work correctly, as even a small breakdown can lead to disastrous effects. Predictive maintenance aims to achieve this goal while also not going overboard in trying to prevent them entirely by combining sensors with predictive techniques that can schedule maintenance when a failure is probable.

    Shebin Jose Jacob’s solution utilizes the Arduino Nano 33 BLE Sense, along with its built-in microphone, to capture audio and predict when a motor is about to fail. He achieved this by first creating a new Edge Impulse project and gathering samples for four classes of sound: OK, anomaly 1, and anomaly 2, as well as general background noise. After designing an impulse and training a classification model on the samples, he was able to achieve an impressive accuracy of about 95% on the test samples.

    The final step involved deploying the model as firmware for the Arduino, which would allow it to classify sounds in real-time by continuously reading from the microphone. Whenever an anomaly is detected, a red LED at the top illuminates.

    You can read more about the project here on its Edge Impulse tutorial.

    The post Predicting potential motor failures just using sound appeared first on Arduino Blog.

    Website: LINK

  • Industrial IoT anomaly detection on microcontrollers

    Industrial IoT anomaly detection on microcontrollers

    Reading Time: 2 minutes

    Arduino Team — July 22nd, 2022

    Consumer IoT (Internet of Things) devices provide convenience and the consequences of a failure are minimal. But industrial IoT (IIoT) devices monitor complex and expensive machinery. When that machinery fails, it can cost serious money. For that reason, it is important that technicians get alerts as soon as an abnormality in operation occurs. That’s why Tomasz Szydlo at AGH University of Science and Technology in Poland researched IIoT anomaly detection techniques for low-cost microcontrollers.

    When you only have a single sensor value to monitor, it is easy to detect an anomaly. For example, it is easy for your car to identify when engine temperature exceeds an acceptable range and then turn on a warning light. But this becomes a serious challenge when a complex machine has many sensors with values that vary depending on conditions and jobs — like a car engine becoming hot because of hard acceleration or high ambient temperatures, as opposed to a cooling problem. 

    In complex scenarios, it is difficult to hard code acceptable ranges to account for every situation. Fortunately, that is exactly the kind of problem that machine learning excels at solving. Machine learning models don’t understand the values they see, but they are very good at recognizing patterns and when values deviate from those patterns. Such a deviation indicates an anomaly that should raise a flag so a technician can look for an issue. 

    Szydlo’s research focuses on running machine learning models on IIoT hardware for this kind of anomaly detection. In his tests, he used an Arduino Nano 33 BLE board as an IIoT accelerometer monitor for a simple USB fan. He employed FogML to create a machine learning model efficient enough to run on the relatively limited hardware of the Nano’s nRF52840 microcontroller.

    The full results are available in Szydlo’s paper, but his experiments were a success. This affordable hardware was able to detect anomalies with the fan speed. This is a simple application, but as Szydlo notes, it is possible to expand the concept to handle more complex machinery.

    Image: arXiv:2206.14265 [cs.LG]

    You can follow any responses to this entry through the RSS 2.0 feed.
    You can leave a response, or trackback from your own site.

    Website: LINK

  • VenTTracker is an anomaly detection system for windows to help ensure proper ventilation

    VenTTracker is an anomaly detection system for windows to help ensure proper ventilation

    Reading Time: 2 minutes

    Arduino Team — August 19th, 2021

    Within an industrial setting, being able to determine if and/or when a machine malfunctions is vital to maintaining safety and uptime. This challenge is what prompted a maker who goes by javagoza on element14 to enter into their Design for a Cause 2021 contest with his device, which he calls the VenTTracker.

    At its heart, the VenTTracker uses an Arduino Nano 33 IoT mounted onto a small protoboard that is attached to a sliding surface, such as a window or vent. Under normal operation, the device does nothing, but once an anomaly is detected, including an obstacle or breakdown, the onboard OLED screen shows an alert message. 

    Because this project uses machine learning to differentiate between normal operation and an anomaly, javagoza collected a large dataset of motions from an accelerometer and then uploaded it to Edge Impulse’s Studio. From there, he added a time series processing block and flattening block to generate the features that fed into the Keras neural network for training and validation. Once deployed back to the Arduino, the model performed very well at telling the difference between the window opening normally and something being in the way.

    He even included Arduino Cloud functionality to display if the window is open and any anomalies that have been detected so far. There was an additional module constructed for environmental monitoring, which consists of a Nano 33 IoT and a BME680 sensor that sends CO2, temperature, and humidity data to another Cloud dashboard to let users know when to open the window.

    To read about the VenTTracker in more detail and see its code, you can visit javagoza’s write-up on element14.

    You can follow any responses to this entry through the RSS 2.0 feed.
    You can leave a response, or trackback from your own site.

    Website: LINK