bands (5 Hz in each bandwidth) are extracted from the frequency domain as defined in Sections 2.3.3.1 and 2.3.3.2.
Additionally, an AEN model is used for reducing the number of forging features; the inputs and outputs are the original and encoded forging load signals, respectively. In this case, the AEN monitors the stability of the bolt‐forming processes and identifies invalid samples, which are mainly affected by the forging pressure.
For example, three failure modes of the bolt‐forming processes and their end products are shown in the upper part of Figure 2.33; these failures usually result from three failure modes including length over‐specification, die notching, and die adhesion. The pressure patterns of the valid process and three failure modes are depicted in the solid and segmented curves in the lower part of Figure 2.33, respectively. Although longer (+0.3 mm) material does not strongly affect the forming process, both die notching and adhesion result in extremely different pressure patterns compared with the original patterns.
Figure 2.33 Failure diagnosis in a forming process.
The single dimension feature in the code of AEN can be used to determine whether invalid bolts have been formed. Because the actual curve of the forging stroke varies, validating the received signal is difficult. Fortunately, the pressure pattern for invalid bolts can be observed using reconstruction of the compressed code by the decoder of AEN with correct results. As shown in the upper part of Figure 2.34, the value for a valid sample is approximately 2.1, whereas that for an invalid sample is approximately −10. The raw data of valid and invalid samples, shown as the dotted curves in the lower part of Figure 2.34, are so similar that the difference could not be identified using a rule or a threshold system.
Figure 2.34 Sample validation using the single dimension feature of the middle layer in the AEN model.
As illustrated in Figure 2.35, an AEN–deep neural network (DNN) is employed to diagnose failures in the forming process. The features extracted by the codes of AEN serve as the DNN model inputs when the reconstructed output X’ of AEN is similar enough to the original input X, which validates that the extracted features of codes are reliable. Consequently, the AEN‐DNN model can not only accurately distinguish valid samples (positive detection rate > 99%) but also correctly diagnose various failure modes (accuracy > 95%). The details of this application case are described in [20] via IEEE DataPort.
Figure 2.35 AEN‐DNN architecture for failure diagnosis.
2.5 Conclusion
This chapter addresses the techniques of data acquisition and preprocessing. For data acquisition, both process data and metrology data have to be collected for developing various intelligent applications. In general, process data consists of sensing signals and manufacturing parameters. As for data preprocessing, the key steps are segmentation, cleaning, and feature extraction. Finally, four practical examples using real‐world data are respectively demonstrated to validate techniques of data acquisition and data preprocessing addressed in this chapter; the detailed experimental data are uploaded in the IEEE DataPort for references.
Appendix 2.A ‐ Abbreviation List
| AC | Alternating Current |
| ADC | Analog‐to‐Digital Converter |
| AE | Acoustic Emission |
| AEN | Autoencoder |
| AI | Artificial Intelligence |
| AIO | Analog Input / Output |
| ANN | Artificial Neural network |
| AOI | Automated Optical Inspection |
| CbM | Condition‐based Maintenance |
| CCD | Charge‐Coupled Device |
| CMM | Coordinate Measuring Machine |
| CNC | Computer Numerical Control |
| CPSs | Cyber‐Physical Systems |
| DC | Direct Current |
| DIO | Digital Input / Output |
| DNN | Deep Neural Network |
| DWT | Discrete Wavelet Transform |
| EtherCAT | Ethernet for Control Automation Technology |
| FFT | Fast Fourier Transform |
| FT | Fourier Transform |
| Industry 4.0 | Fourth Industrial Revolution |
| IIoT | Industrial Internet of Things |
| MRA | Multi‐Resolution Analysis |
| MQTT | Message Queuing Telemetry Transport |
| NC | Numerical Control |
| OPC‐UA | Open Platform Communication Unified Architecture |
| PC | Personal Computer |
| PLC | Programmable Logic Controller |
| PM | Preventive Maintenance |
| RM |
|