## 4 Results
### Cutting to Cycle Waveforms  
Data collection experiments using SD cards have enabled vibration waveforms on the testbed to be obtained. However, as these data are one continuous waveform for each tightening torque, it is not possible to analyse them from the point of view of which part of a cycle the change appears. In this evaluation experiment, a cut-out algorithm for cycle waveforms is proposed to enable comparison between testbed reciprocating cycles from a single continuous vibration waveform, and its usefulness is demonstrated by applying it to the measured norm waveforms. The cycle waveform cut-out algorithm used in this research consists of the following steps:  
1. Detection of outages between cycles
2. Detection of cycle start point
3. Data cut-out at cycle length  
In the detection of the stop state between cycles, the range of values of the vibration waveform at the time of the ball screw stop is first defined from the graph of the norm waveform. Next, the part where the value within the defined norm range at the time of ball screw stop continues for a certain period of time or longer is judged to be the stop state of the ball screw. The stop state determined here includes both the stop when the ball screw stage is at the right end and the stop when the stage is at the left end. The fact that the time for the stage to stop at the left end is longer than the time for the stage to stop at the right end in each cycle is used. In other words, a stop at the left end is judged to be a stop when the duration of the stop is longer than the stop at the right end of the ball screw. Stopping at the left end can be treated as stopping between cycles, and thus the stopping state between cycles can be detected.  
After detecting the stop between cycles, the cycle start point is detected. Here, the fact that the stop always takes place at the left-hand end before the start of the cycle is used. In other words, a new cycle of the ball screw is judged to have started when a norm exceeding the norm range at the time of the ball screw stop is detected immediately after the detected stop state between cycles. With this method, even if a norm exceeding the range of the norm at ball screw stop is observed as noise during the stop at the right end or during the stop at the left end, it does not satisfy the condition that it is immediately after the stop state between cycles, so it does not incorrectly cut out the norm.  
Finally, the data are cut out at a fixed length of cycle from the detected cycle start point. Here, the cycle length is defined manually from the norm waveform as well as the norm range when the ball screw stops. The fixed-length cut-out has the disadvantage that it cannot cope with dynamically changing cycle lengths. However, fixed-length cut-outs are an advantage when similar products are repeatedly manufactured at the same cycle length on the production floor, as changes in the approximate shape of the cut-out waveform can detect anomalies in manufacturing, even though the cut-outs are made at the same cycle length.  
This cut-out algorithm was applied to the norm waveforms obtained in the data collection experiment using the SD card. The cycle waveforms at a tightening torque of \(11\) N are shown in Figure 16 and at a tightening torque of \(1\) N in Figure 17. The experimental results confirmed that each of the extracted waveforms was a cycle-by-cycle waveform, with the vibration matching the cyclic motion of the testbed. The use of the norm of triaxial acceleration, rather than triaxial acceleration itself, as the data to be extracted enables extraction without having to set the range of the stop state for each axis.