## 4 Conclusions  
In summary, force data from the six-die nut manufacturing machine was analysed by the force profile correlation analysis and the force SRS profile correlation analysis to identify faults related to degradation in machine condition. The data allowed identification of fault events that changed the force impact due (a) to misalignment between the die and workpiece and (b) to differences in the surface area contact between the die and the workpiece caused by deformation of die and non-die components. Both correlation methods were successful as the nut manufacturing process requires continuous, repetitive, reciprocating movements that allowed force impact changes to be identified. Of the two profile correlations, the force SRS profile correlation analysis was more sensitive and showed faults quicker, since the peak impact energy of the SRS was given for each individual frequency. When the die experiences wear, the impact energy decreases for each frequency. The same is reflected when the quality is affected due to the dimensional tolerance change of the nut mould. Therefore, force data and the SRS of the data can be applied to a repetitive impact manufacturing machine. Additionally, the force data analysis provided more precise results with greater computational simplicity and much less filtering than vibration data due, in part, to the proximity sensors, which triggered data collection and reduced random noise. Furthermore, the primary operation of the nut machine is based on the force exerted by the dies, while vibration is a secondary effect generated by the force impact. Clear empirical and statistical determination of thresholds were established to distinguish healthy versus faulty machine operation. Health thresholds are specific to each die and type of nut manufacturing machine. However, the analytical methodology discussed in this paper remains applicable for other industrial nut manufacturing machines. It may also be implemented in other machines that have a repetitive, reciprocating, manufacturing process such as forging. Additionally, use of data from a machine in commercial operation instead of a sterile testing environment means the analysis is realistic and feasible to adopt industrially. The implementation of timely maintenance for this industry can maintain product quality, reduce machine downtime, keep production schedules on track, reduce maintenance costs, more efficiently utilise labour resources and help to maintain competitive trading advantage, as the nut manufacturing industry continues to see growth in exports. Future work would entail developing a real-time algorithm to monitor machine condition using the die health thresholds of the force SRS signal profile correlation to provide alerts on fault occurrences. It would also be useful to investigate further the possibility of categorising fault types, even if on a broad basis. Conceptualization, H.-Y.C. and X.E.A.N. ; methodology, H.-Y.C. ; software, X.E.A.N. ; validation, H.-Y.C., X.E.A.N. ; formal analysis, X.E.A.N. ; investigation, X.E.A.N. ; resources, H.-Y.C. ; data curation, X.E.A.N. ; writing--original draft preparation, X.E.A.N. ; writing--review and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fault Types** & **Die** & \
\begin{tabular}{c} **Approximate Average Force** \\ **Profile Correlation Warning Time** \\ \end{tabular} &
\begin{tabular}{c} **Approximate Average Force SRS** \\ **ProfileCorrelation Warning Time** \\ \end{tabular} \\ \hline Quality Adjustment and & 4P & 2 h 40 min & 3 h 10 min \\ Non-Die Damage & 9P & 2 h & 2 h \\ \hline Die Damage & 4P & 2 h 30 min & 3 h \\
2 h & 9P & 2 h & 2 h 31 min \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Approximate warning times prior to maintenance for 4P and 9P force and force SRS profiles in industrial application. editing, H.-Y.C. ; visualization, X.E.A.N. ; supervision, H.-Y.C. ; project administration, H.-Y.C. ; funding acquisition, H.-Y.C. All authors have read and agreed to the published version of the manuscript.