###### Abstract  
Nowadays, fault diagnostics is widely applied under Industry 4.0 to reduce machine maintenance costs, improve productivity, and increase machine availability. However, fault diagnostics are mostly post-mortem. When the fault is identified, it is already too late because damages have been done to the product and machine. This paper compares the efficacy of several signal data processing techniques for detecting faults that are about to occur. Our aim is to find an efficient way to predict the fault before it occurs. A continuous wavelet transform synchrosqueezed scalogram was found to be most suitable for this purpose, but it is difficult to apply. A novel procedure is proposed to count the number of pulses in the synchrosqueezed scalogram. A new method for detecting the trend from the pulse counts is then developed to predict the fault before it happens. The procedure and method are illustrated with experimental data collected while running an automated double-thread trampoline webbing machine.  
Statistical Process Control (SPC); Fast Fourier Transform (FFT); continuous wavelet transform (CWT); synchrosqueezed wavelet transform; scalogram; pulse time graphs +
Footnote †: journal: 16 December 2021  
monitoring system using machine learning models on features derived from both time and frequency domain data. The primary goal was to imitate human experience and expertise in recognizing symptoms of fault. Hybrid time-frequency methods have the advantage of both time and frequency domain methods but suffer from the complexity and errors in simulated responses compared to experimental data.  
Fault diagnostics research can be explored in two types: (1) identification of faults that have already occurred so that appropriate remedial actions can be taken to quickly rectify the problem [5]; (2) incipient fault detection to detect a machinery fault at its early stages and take actions before it develops into catastrophic faults during the manufacturing process [6]. Both types of fault diagnostics share similar analysis methods, but the approach and data requirements are different.  
The first type of fault diagnostics methods aims to rectify problems. It is a post-mortem approach. The second type of incipient fault diagnostics is to detect if the machine, which is still working well, has the trend to develop a fault soon. It generally requires a good system model to support fault prediction. This is not always possible due to complexity or age of the system. On the other hand, using captured time-based signals, the time window for trend diagnostics varies greatly depending on the nature of the manufacturing process. Hence, the efficacy of analysis methods varies, affecting reliability of the diagnostics outcomes.  
This paper uses an automatic trampoline webbing machine as the study platform to examine the efficacy of different methods. Signals captured by the digital sensors on the webbing machine were analysed, and fault trend indicated from the processed information was compared. Based on the evaluation, a new fault trending algorithm that combines digital techniques, including wavelet transform and scalogram, to determine if performance of the automatic webbing machine starts to deteriorate. The innovative system has the benefit of quickly classifying the fault signal type and identifying the time-frequency information so that the operator can manipulate the machine settings based on time-frequency information to avoid further faults happening.