Use a current sensor to efficiently acquire data for predictive maintenance with AI
implemented using components from the author’s treasure chest of spare parts (Figure 5). Readily available equivalents would be as follows: ■ Adafruit 64 breadboard ■ Twin Industries TW-E012-000 pre-formed wire kit for use with breadboards ■ Stackpole Electronics RNMF14FTC150R 150 ohm (Ω) ±1% 0.25 watt (W) through-hole resistor ■ Stackpole Electronics’ RNF14FTD10K0 10 kiloohm (kΩ) ±1% 0.25 W through-hole resistor ■ KEMET ESK106M063AC3FA 10 microfarad (µF) 63 volt aluminum electrolytic capacitor With regard to the leads from the current sensor, 1931 22-28 AWG crimp pins from Pololu Corp. were crimped on the ends. These pins were subsequently inserted into a 1904 5 x 1 black rectangular housing with a 0.1 inch (in.) (2.54 millimeter (mm)) pitch, also from Pololu.
Figure 4: The 1-foot extension power cord that was modified to accept the current sensor. Image source: Max Maxfield
Figure 6: Comparison of good/normal data (top) and bad/abnormal data (bottom). Apart from the differences in color, these don’t seem terribly different to the human eye, but an appropriate AI/ML model can distinguish between them. Image source: Max Maxfield
occur. However, the hardware used to implement the predictive maintenance system needs to be as simple and cost-effective as possible; also, designers need ready access to the required software to perform the analysis. As shown, instead of opting for a complex multi-axis accelerometer and associated hardware, a simple, low-cost, small-size, CR3111-3000 split-core current transformer connected to a low- cost microcontroller platform can perform the required sensing and data gathering. Coupled with advances in AI/ML tools and algorithms, it’s now possible for non-AI/ML experts to create sophisticated AI/ML models that can be deployed in a wide range
the beginning and end of the run), and then loaded into NanoEdge AI Studio. The good data was collected with the vacuum pump running in its normal mode. In order to gather the bad data, the pump’s air filter was obstructed with a disk of paper. Using the good and bad data, NanoEdge AI Studio generates the best AI/ML library solution out of 500 million possible combinations. Its ongoing progress is displayed in a variety of different ways, including a scatter chart showing how well the normal signals (blue) are being distinguished from the abnormal signals (red) with regard to a threshold value, which was set to 90% in this example (Figure 7). The early models typically find it difficult to distinguish between the normal and abnormal data,
processor being used (an Arm Cortex-M0+ in the case of the Arduino Nano 33 IoT development board), the type(s) of sensor being used (a current sensor in this case), and the maximum amount of memory that is to be devoted to this AI/ML model (6 Kbytes was selected for this demonstration). In order to create the AI/ML model, it is first necessary to capture representative samples of good and bad data (Figure 6). A simple Arduino sketch (program) was created to read values from the current sensor. This data can be directly loaded into NanoEdge AI Studio ‘on-the-fly’ from the microcontroller’s USB port. Alternatively, the data can be captured into a text file, edited (to remove spurious samples at
but the system evaluates different combinations of algorithmic elements, iterating on increasingly accurate solutions. In this case, the process was halted after 58,252 libraries had been evaluated. The resulting library (model) was only 2 Kbytes in size. It’s important to note that, at this stage, the model is in its untrained form. Many different factors may affect the ways in which the machines run. For example, two seemingly identical vacuum pumps could be mounted in different locations – for example, one on a concrete slab and the other on a suspended floor. Or one of the machines could be located in a hot, humid environment, while the other may be in a cold, dry setting. Furthermore, one could be connected to long lengths of metal pipe, while the other could be attached to short lengths of plastic pipe. Thus, the next step is to incorporate the library into the applications running on the microcontrollers and sensors that are attached to machines that are deployed in the
real world. The AI/ML models on the different machines will then train themselves using good data from these real-world installations. Following this self-training period, the AI/ML models can be left to monitor the health of the machines, looking for anomalies and trends, and reporting their findings and predictions to human supervisors. Conclusion Predictive maintenance using AI/ ML allows engineers to address problems before failures actually
of simple and complex sensing applications.
Creating the AI/ML application
Figure 7: NanoEdge AI Studio evaluates up to 500 million different AI/ML models to determine the optimal configuration for the normal and abnormal data. The initial models are rarely successful (top), but the tool automatically iterates on better and better solutions until the developer decides to call a halt (bottom). Image source: Max Maxfield
In order to create the AI/ML application, a free trial version of NanoEdge AI Studio was accessed from Cartesium’s website (see also, ‘ Easily Bring Artificial Intelligence to Any Industrial System ’). When NanoEdge AI Studio is launched, the user is invited to create and name a new project. The user is then queried as to the
Figure 5: The prototype circuit was implemented using a small breadboard and components from the author’s treasure chest of spare parts. Image source: Max Maxfield
we get technical
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