Artificial Intelligence Enabling Denoising in Passive Electronic Filtering Circuits for Industry 5.0 Machines
Abstract
:1. Introduction
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- The Material and Methods Section discussing the circuital low-pass filtering model and the AI-ANN/RF workflow adopted for the data processing; the section provides the proof of concept of the AI controlled black box double-stage RC filter influenced by white noise, information about the adopted tools, and a description of the basic voltage signals;
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- The Results Section describing circuit simulation through a parametric analysis modeling different effects of the white noise, and the AI results and performances; the section highlights the parametric circuital responses of the analyzed filter by modulating the white noise amplitude and predicting by means of the ANN and RF algorithms the voltage output enabling a possible switching condition on a further RC stage (second stage) to denoise the voltage output; furthermore, the section provides performance results proving the correct choice of the ANN and RF algorithms for the proposed study;
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- The Discussion Section describing the advantages, perspectives, disadvantages, and limitations of the proposed model, and the new protocol to follow in order to use a double-stage low-pass filtering network; the protocol allows us to understand better the proof of concept of the switching circuit selecting the single or double RC filtering stage;
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- The Conclusions Section highlighting the results.
2. Materials and Methods
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- Input data: input dataset for the training and testing of the AI supervised algorithms;
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- Data pre-processing: data cleaning and manipulation providing the final dataset to process by the AI algorithms;
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- Data processing and reporting: AI data processing, data visualization, and algorithm performance.
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- ‘File Reader’: data input importing the output voltages of the parametric analysis (output of the first RC filtering stage) changing with the Rx noise parameter;
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- ‘Normalizer’: scaling of the input voltage data from values ranging from 0 to 1 (decrease in the computational error);
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- ‘Column Appender’: merging operation of the all input data into an unique data table to be processed by the ANN and RF algorithms;
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- ‘Row Filter’: filter preparing the best database to process (the final record number to process is 2409);
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- ‘Partitioning’: block able to structure the training and the testing dataset (randomly distributed with 70% in the training dataset and the remaining 30% in the testing dataset);
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- ‘RProp MLP Learner’: block implementing an ANN multilayer perceptron (MLP) learning algorithm using the RProp optimizer;
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- ‘MultiLayer Perceptron Predictor’: node adopted for the ANN testing data processing;
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- ‘Random Forest Learner’: RF algorithm learner block;
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- ‘Rando Forest Predictor’: RF algorithm testing block;
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- ‘Numeric Scorer’: node estimating algorithm performances;
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- ‘Line Plot’: dashboard visualizing ANN and RF results;
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- ‘X-Partitioner’: node used for the cross-validation loop; the dataset is divided into different groups named folds (for the specific case, 10 folds are considered);
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- ‘X-Aggregator’: at the end of the loop, the ‘X-Aggregator’ node aggregates the results from each iteration; all nodes between the ‘X-Partitioner’ and the ‘X-Aggregator’ node are iteratively executed; the block compares the predicted classes with the real classes for all rows providing iteration statistics.
3. Results
3.1. White Noise and RC Filtering: LTSpice Simulation
3.2. ANN and RF Noisy Signal Prediction: Predicted Signal Trends and Algorithm Performances
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- Fix the minimum number of neurons per layer (in the proposed case, the minimum number is 10 corresponding to the number of the input nodes);
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- Fix the number of epochs enough to complete the algorithm’s iterations (in the analyzed case the epochs are 400);
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- Vary the number of hidden layers, finding the minimum error condition (see Figure 9, finding the minimum condition for 18 hidden layers).
3.3. Denoising Optimization through the Second Compensatory RC Stage
4. Discussion
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- Step 1: the first RC stage is able to read possible noisy voltage outputs; the outputs are adopted for the AI training and testing processes (creation of the historical dataset);
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- Step 2: ANN/RF predictions provide the envelopes of the predicted voltages and the predicted signal trend;
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- Step 3b: if the envelopes are regular, no further filtering is required and the machine is correctly controlled (the ‘0’ command of the circuit of Figure 1);
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- Step 3a/Step 4a: if the envelopes are different from the expected voltage envelopes (voltage envelopes without the noise effect), then the circuit simulation is executed to find the best R and C component of the second stage filtering compensatory network (Step 3a); the best R and C components are selected dynamically, and the further compensatory RC filtering network is applied (the ‘1’ command of the circuit of Figure 1).
5. Conclusions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
Appendix C
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Method | MSE | MAE |
---|---|---|
ANN | 0.008 | 0.06 |
RF | 0.01 | 0.065 |
Fold | MSE |
---|---|
Fold0 | 0.012 |
Fold1 | 0.01 |
Fold2 | 0.014 |
Fold3 | 0.013 |
Fold4 | 0.015 |
Fold5 | 0.015 |
Fold6 | 0.008 |
Fold7 | 0.011 |
Fold8 | 0.013 |
Fold9 | 0.012 |
Proposed Modeling in Different Methodologies of Analysis | Advantages of Implementing White Noise | Disadvantages of Implementing White Noise |
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Generic signal processing | In signal processing, white noise is applied to check machinery systems and the frequency behavior of electronic circuits and to prevent quantization errors (a typical application is in high voltage transformers [29]). AI could facilitate the understanding of the signal behaviors in the presence of white noise through the classifications of noisy signals. | Signal processing requires an accurate check system measuring the noise effect with a high sensitivity (high sensitivity also in measuring low-intensity noises). In this direction, the white noise could strongly amplify the error, distorting the system response, and a high sensitivity model may not be suitable for the denoising process. Furthermore, AI algorithms could be useless when the noise completely covers the signal. |
Time-series analysis | In time-series analysis, white noise is used to estimate residuals and errors, by considering residuals as random fluctuations in machine circuits. Furthermore, AI could predict the time-domain signal behavior to prevent possible corrective actions, thus decreasing the risk of machine breakdown. | Regarding this methodology, a main disadvantage is the different time sampling of the electrical signals according with the adopted circuit simulation tool: this could reduce the number of records to be used for the training model and could require dataset manipulation to obtain a homogeneous dataset to process (filtering of empty records). |
Statistics | White noise is a baseline in many statistical models to compare other signals. Statistics can be compared with AI results finding matching results supporting the whole analysis. | AI is unnecessary for data processing requiring simple static approaches to study white noise responses. |
Interference analysis | White noise is adopted to study interferences in an analogic systems and to prevent crosstalk [30]. AI could be suitable to classify the different interferences influencing machines. | The origins of circuit interferences are difficult to determine. In this direction, the AI-based black-box approach is appropriate to describe the circuital behavior without the knowledge of the interference which can be modeled in a generic way by white noise. |
Machine control systems | Additive white noise and disturbances characterized by specific carriers are useful to analyze disturbances in the Proportional–Integral–Derivative (PID) circuits controlling machines. AI algorithms could support corrective actions as for passive RC filtering networks coupled with PID systems. | There are limitations in the identification of possible white noise effects in each element of the PID control system (individually affecting the P or I or D element). AI algorithms could be useless to differentiate the noise effect on each individual element. |
Digital Twin (DT) simulations | White noise and AI are essential to structure a DT model able to simulate the real behavior of machines working in manufacturing processes by predicting breakage risks. | DT models should be accurately tested for the setting of efficient testbeds to associate with a specific machine or production line. |
Technological Limits | Technology Description | Technological Perspectives |
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Application for RC filtering network | The methodology is applied for specific double-stage RC circuits controlling machines. More complex systems could be controlled by PID systems combined with appropriate filtering networks. | Extension of application fields for more complex machines or components including drones [31], smart building [32], and more complex optoelectronic systems [33,34]. |
Passive noise control | The proposed approach takes into account a passive network and not considers electrically powered equipment or components. An active network uses electrically powered equipment or components to route the signal and could be more efficient for the whole denoising process. | Possible perspectives are the adoption of active systems for active noise control [35], CNN active noise control [36] (as for acoustic signal processing), generative fixed-filters [37], and virtual sensing active noise control system [38]. |
Technique to command switching circuits | The proposed approach is based on the concept to control circuits by switches controlled by commands (‘0’ or ‘1’ values). | Advanced control switching systems could be achieved by adopting innovative approaches as switching noise suppression with Multisampled Pulsewidth Modulation technique (MS-PWM) [39]. |
Difficulty to distinguish the origins of the white noise influencing the machine control circuits | Usually the origin of the white noise is unknown. The causes can be casual or intentional. | In the case of intentional causes as for cybersecurity applications, the proposed model can be applied to find possible analog Trojans modifying the electrical behavior of the control system [40,41,42], or in general to detect attacks in radio frequency environments. |
Functionalities | Proposed Model | Model Proposed in Reference [13] |
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AI prediction of sinusoidal signals | √ | √ |
Black-box modeling | √ | √ |
Model applicable for different topologies of circuits | √ | × (only for amplifier) |
Multi stage circuit model | √ | × |
White noise implementation | √ | × |
Feedback principle defining the switching control condition of an intelligent circuit denoising the output signal | √ | × |
AI setting for the prediction of random noise generalizing a generic noise effect | √ | × |
Estimation of the signal output envelopes to compensate automatically the noise effect (denoising process) | √ | × |
Multiplicative noise modeling and simulation | √ | × |
Modeling and simulation of the modulation of the noise intensity (parametric analysis) | √ | × |
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Massaro, A. Artificial Intelligence Enabling Denoising in Passive Electronic Filtering Circuits for Industry 5.0 Machines. Machines 2024, 12, 551. https://doi.org/10.3390/machines12080551
Massaro A. Artificial Intelligence Enabling Denoising in Passive Electronic Filtering Circuits for Industry 5.0 Machines. Machines. 2024; 12(8):551. https://doi.org/10.3390/machines12080551
Chicago/Turabian StyleMassaro, Alessandro. 2024. "Artificial Intelligence Enabling Denoising in Passive Electronic Filtering Circuits for Industry 5.0 Machines" Machines 12, no. 8: 551. https://doi.org/10.3390/machines12080551