## 1 Introduction
Accessing the dies to check wear is difficult due to the machine design, so some companies have implemented a policy to replace dies and tighten the dimensional tolerance screw at the base of the machine after a fixed amount of production. Notwithstanding these policies, unexpected damage may still occur between maintenance periods and could result in extensive and costly repairs, downtime, and faulty products. This research is an improvement to a previous study that analysed vibration data of the six-die nut manufacturing machine to identify indicators of impending faults [19]. In the industry, nuts are produced by the deformation of the raw material through a series of die impacts [20]. Each deformation is created by a different force value with the first impact being the smallest and the last impact being the largest, since the latter removes the centre material from the workpiece to accommodate the threading. As a result, the fourth, fifth, and sixth dies experience frequent damage. Given the preponderance of forces in the production process, it was considered that analysing the force, rather than vibration, of die impacts should yield better information on fault indicators. The force analysis was facilitated by force sensors already installed in the machine. Force data has been used to determine tool wear in the high-speed machining process [21]. According to a dissertation written by [PERSON] and [PERSON], it has been used to detect faults in electric motors [22]. Another study used the cutting tool force of a lathe machine to monitor tool wear through statistical processing of its wavelet transform to differentiate among initial wear, normal wear, and severe wear [23]. Similar to the previous study, the health wear status of a cutting tool was monitored via cutting forces and vibration signal analyses to determine when faults developed on the cutting tool through an algorithm developed in MATLAB [24]. Since force sensors are robust and reliable, another study extracted harmonic features from force signals of tools with different wear in order to select the most prominent wear indicator features to input into a tool condition monitoring system [25]. Most studies on tool condition conducted with force data have been performed on rotating tools as opposed to a reciprocating motion machine. Given the different levels of force impacts--also known as'shocks'--that are transmitted to the workpiece by the die to deform the raw material, the shock response spectrum (SRS) was considered the most appropriate tool to analyse the force data, because it es timates the dynamic response of structures [26]. Furthermore, this technique has been used successfully to identify faults within components as well as to assess the severity of damage dealt to a workpiece or components in a machine. In one research project, the SRS analysis was used to assess the severity of damage caused by water hammer loads produced by an external impact due to fluid structure interaction [27]. The SRS technique was also used to extract features which characterised different types of faults in a gearbox [28]. In another study, it was used to form damage boundaries for two failure modes of a crystal oscillator--an electric component used in a circuit--by identifying the value of the shock response in a certain frequency range [29]. The most recent study used the SRS of vibration data to calculate a health indicator and thus determine the rehplicating time for the toggle pin of a plastic injection moulding machine [30]. Apart from the SRS technique, correlation has been applied to various fault detection studies by relating two different series to determine how similar they are. As the nut manufacturing machine creates nuts with a reciprocating repetitive motion, correlation was suitable for this study. A study by [PERSON] et al. [31] showed that the technique had great potential in identifying defects in industrial gearboxes. In 2014, [PERSON] et al.