## 6 Discussion and Conclusions  
Most of the previous studies about equipment monitoring were conducted under the assumption that labels indicating equipment failures could also be collected when collecting data. In cases where data were collected without labels, research was mainly conducted to explore outliers rather than equipment monitoring problems. In the former case, various types of failure were arbitrarily generated in the laboratory; failure labels were assigned to the equipment data collected, and a supervised learning algorithm was applied. However, because various types of failure occur intermittently in the field, labeled data sufficient for supervised learning is rarely collected. Additionally, because the results obtained from research on detecting outliers in equipment data do not indicate equipment failure, fault alarms are often generated. In the case of equipment condition monitoring using the pseudo-label technique proposed in this paper, equipment data collected in unlabeled form were targeted, but normal/abnormal pseudo-labels were assigned to enable the use of various classification algorithms. We proposed a procedure for generating an alarm by monitoring the performance change trends in various classifiers, and we verified its usefulness by applying it to data collected after equipment failure. In this paper, we briefly reviewed recent research to diagnose equipment status based on classification learning. We collected vibration signals from a test rig manufactured to resemble field equipment, assigned pseudo-labels with the Euler method, and observed changes in the classification performance index of the CNN classifier. In summary, this article contributed to and achieved the following results. First, we proposed assigning pseudo-labels to vibration data collected without labels regarding abnormal equipment conditions. Based on a labeled dataset, we learned a CNN classifier and analyzed the classification accuracy of the model. An alarm is generated if the classification accuracy surpasses 0.80 and a warning signal if it surpasses 0.95. Experimental data were divided into 10 sections called Steps. Using pseudo-labels in Steps 1 to 3, changes in equipment status were detected based on monitoring classification accuracy. In Steps 4 to 8, there was no significant change in status, but in Step 9, a catastrophic failure occurred, and classification accuracy rapidly changed. In this paper, it was confirmed that the \(d\) value is one of the parameters of the Euler method that affects the sensitivity of capturing changes in the state of equipment. Results showed that when interval parameter \(d=4\), the change in accuracy was greater than when \(d=1\), and alarm and warning performance was also better. The interval parameter is related to latency in the effect of an equipment failure, and it is important to monitor equipment status efficiently. A classification-based equipment status diagnosis model using pseudo-labels provides meaningful alarm and warning information for each step, but why the alarm occurred is unknown. A promising future study could apply AI to explain the reason the CNN classifier judged the data to be normal or abnormal. If classifier performance is improved by activating a specific frequency region in the CWT image of the vibration signal, fault identification will be possible beyond merely detecting the fault. In the classification-based equipment status diagnosis model, meaningful alarm information was provided for each step, but as future research topics, explanatory research is required for the process of separately extracting and selecting time domain and frequency domain features that can explain changes. Conceptualization, M.-K.S. and W.-Y.Y. ; methodology, M.-K.S. and W.-Y.Y. ; software, M.-K.S. ; formal analysis, M.-K.S. ; investigation, M.-K.S. ; resources, M.-K.S. ; data curation, M.-K.S. ; writing--original draft, M.-K.S. and W.-Y.Y. ; writing--review and editing, W.-Y.Y. ; visualization, M.-K.S. ; supervision, W.-Y.Y. ; project administration, W.-Y.Y.