## 3 Experimental Settings  
Experiments were conducted on an automatic trampoline weaving machine. Similar to the traditional loom, two manufacturing processes take place on the machine: the warp preparation and weft insertion. Our experiment focused on the faults that occurred during the weft insertion process. There are two sets of hooks at both sides of the machine. Figure 1 shows the hooks mounted on the strainer. The thread is tied to the other side in similar fashion. This process is currently manual. Figure 1a,c display the strainer and hooks on the machine, while Figure 1b,d show the rack and pinion mechanism, and how the yarn threads are tied to the hooks.  
The next step is the weft insertion process, which is equivalent to weaving. When the weft is picked from the weft package, the rapier will carry the weft to cross the width of the warp. The thread is hooked at the end of the rapier movement so that a double threaded weft is formed. The comb pushes forward to lock the double to the trampoline. The two heddles change its position ready for the next weft.  
The weft insertion process has been automated [46] such that the rapier is controlled by a stepper motor; the rapier then repetitively carries the weft yarn from one side of the machine to the other, completing the weft insertion process. Although the stepper motor and the rack mechanism provide a strong support for the rapier's movement, the slip and dislocation between rack and rapier as well as the missing hook on the other side of the machine still occur randomly. In a manual machine, this missing hook will be observed by the operator and corrected immediately. However, in the new automatic machine, if the missing hook occurs in the middle of the matt, the machine will continue. Unfortunately, the correction of the missing hook requires that the matt be de-threaded until the missing hook is not a good match.  
Figure 1: Hooks are mounted on a strainer: (**a**) The strainer rotates to wind the trampoline mattress during the weaving process, (**b**) rapier is driven by a rack and pinion mechanism, (**c**) hooks are mounted on a strainer, (**d**) yarn thread is tied to the hook one by one at both sides of the machine.  
hook point and re-manufactured from that point. The re-work process is extremely time consuming and distressed.  
The objective of this paper is to find a trend detection method that can predict the fault during weft insertion process. This trend detection method should indicate that the system is on the path of approaching a fault condition so that the operator knows the exact time and can take appropriate action. To achieve this, a signal collecting system is needed on the machine, in this case, a vibration signal on the machine's major moving part, i.e., the rapier.  
To simulate the onset of a fault, the guide to the yarn was slowly tightened. This situation is a common scenario when undesirable friction is built up over a period inducing different tensional forces. The forces were very small, but the movement of the rapier was slightly affected. Hence, it is expected that analysing vibration signals on the rapier before and after a fault occurring could provide the basis for comparing efficacy of different signal diagnostic methods detecting the trend of faults.  
To acquire signals from the manufacturing process, an LSM 303 DLHC accelerometer on an Arduino Mega 2560 board is installed on the rapier (Figure 2). As the rapier moves back and forth, vibration signals are transmitted to the host station by Wi-Fi. Twenty minutes was the time length of one data set. Each data set therefore contains more than 2000 points. The experiments were repeated to acquire a reasonable number of data sets. A data record was marked normal if there was no fault observed. A data record was marked abnormal if faults such as a missed hook or loose thread was observed. These data sets were then processed by the algorithms described in the following sections.