Use a current sensor to efficiently acquire data for predictive maintenance with AI Written by Clive ‘Max’ Maxfield
Arduino Nano 33 IoT also comes equipped with both Wi-Fi and Bluetooth connectivity.
or greater current values). All members of the family support a frequency range of 20 hertz (Hz) to 1 kilohertz (kHz), covering the majority of industrial applications. Also, all CR31xx devices employ a hinge and locking snap that allows them to be attached without interrupting the current carrying wire. The Arduino Nano 33 IoT One example of a low-cost microcontroller development platform suitable for prototyping simple AI/ML applications is the ABX00032 Arduino Nano 33 IoT from Arduino (Figure 2). Featuring an Arm Cortex-M0+ 32-bit ATSAMD21G18A processor running at 48 megahertz (MHz) with 256 kilobytes (Kbytes) of flash memory and 32 Kbytes of SRAM, the
Data capture circuit The circuit used for the purpose of this discussion is shown below in Figure 3. The CR3111-3000 transforms the measured current driving the machine into a much smaller one using a 1000:1 ratio. Resistor R3, which is connected across the CR3111-3000’s secondary (output) coil, acts as a burden resistor, producing an output voltage proportional to the resistor value, based on the amount of current flowing through it. Resistors R1 and R2 act as a voltage divider, forming a ‘virtual ground’ with a value of 1.65 volts. This allows the values from the CR111-3000 to swing positive and negative and still not hit a rail, since the microcontroller cannot accept negative voltages. Capacitor C1 forms part of an RC noise filter that reduces noise from the 3.3 volt
supply and nearby stray fields from getting into the measurements, thereby helping the voltage divider act as a better ground. A vacuum pump with an integrated filter was used to provide a demonstration test bench. For the purposes of this prototype, Tripp Lite’s P006-001 1 foot (ft.) extension power cord was inserted between the power supply and the vacuum pump (Figure 4). Figure 2: The Arduino ABX00032 Nano 33 IoT provides a low-cost platform upon which to build AI/ML applications to enhance existing devices (and create new ones) to be part of the IoT. Image source: Arduino
The Internet of Things (IoT) has brought about tremendous interest in using artificial intelligence (AI) and machine learning (ML) technologies to monitor the health of machines including motors, generators, and pumps, and to alert maintenance engineers as to any looming problems. One difficulty for the designers of AI/ML systems looking to implement this type of predictive maintenance is selecting the best sensor for the application. Another issue is that relatively few designers have any experience creating AI/ML applications. To obtain the data for the AI/ML system to act upon, designers often opt for sophisticated sensors like three-axis accelerometers coupled with high-powered microcontroller development platforms. In many cases, however, it’s possible to achieve the desired goal using a simple current sensor in conjunction with a more modest and less costly microcontroller development platform.
This article introduces the idea of using a current sense transformer to obtain the data required to simply and cost-effectively implement AI/ML applications. Using a low-cost Arduino IoT microcontroller development platform and a current sense transformer from CR Magnetics, the article also presents a simple circuit that employs the current sensor to monitor the health of a vacuum pump with an integrated filter, alerting the user when the filter has become clogged. Finally, the article presents an overview of the process of creating the associated AI/ML application.
avoid this complexity, it’s worth remembering that everything is interrelated. Just as an injury to one part of a person’s body can cause referred pain that is perceived elsewhere in the body, a failing bearing in a motor can modify the current being used to drive that motor. Similarly, in addition to causing overheating, a blocked air intake can also modify the current being used to drive the motor. Consequently, monitoring one aspect of a machine’s operation may cast light on other facets of its workings. As a result, it’s possible to achieve the desired monitoring and sensing goal by observing a related parameter using a substantially simpler sensor, such as the low-cost, small-size, CR3111- 3000 split-core current sense transformer from CR Magnetics (Figure 1). The CR3111-3000 can be used to detect current up to 100 amperes (A) (other members of the CR31xx family can be employed for lessor
The prototype circuit was
Figure 3: The circuit used to convert the output from the CR3111-3000 into a form that can be used by the Arduino Nano 33 IoT with its 3.3 volt inputs. Image source: Max Maxfield
Simple sensors for AI/ML
In order to acquire the data for an AI/ML application to act upon, designers often opt for sophisticated sensors like three- axis accelerometers; but this type of sensor can generate vast amounts of data that are difficult to manipulate and understand. To
Figure 1: The CR3111-3000 split-core current sense transformer provides a low-cost, easy-to-use current detector that can be employed as the primary sensor in an AI/ML predictive maintenance application. Image source: CR Magnetics
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