## 1 Introduction
### Literature Review and Research Gaps  
A rising need in industry for a lightweight and cost-effective solution to fault detection as a result of Industry 4.0 has encouraged the development of soft sensing systems, which use the existing sensors in a system to infer further information regarding the system. Statistical analysis and signal processing are frequently used methods in the field of anomaly detection. As a method of soft sensing, they are able to overcome the drawbacks of physical hardware monitoring and provide a robust method of data inference. For instance, in Ref. [5] a dynamic model is proposed that is able to utilise the existing Supervisory Control And Data Acquisition (SCADA) system in wind turbines to dynamically model the relationship between the sensor readings by a parameter estimation process for the purpose of fault detection. A frequency domain analysis is used to determine damage sensitive indices, which are then compared to the model sensor. The technique is tested on a 5-year wind turbine dataset where the system was able to detect faults as well as perform fault prognosis. Whilst the method is clearly effective in the specified use case, the flexibility of the method for other use cases comes into question as in-depth knowledge about the system and the relationships between the variables being analysed was utilised to be able to create the initial model. This issue is also mentioned in Ref. [6], where the authors concluded from their survey of outlier detection techniques that model-driven methods are heavily dependent on the understanding of the data being analysed. The lack of flexibility of such techniques is also mentioned, due to the heavy tailoring that must be made to the models for each dataset. This is a trend across many signal processing techniques, including for motor condition monitoring, where [PERSON] [7] noted in their state-of-the art review of outlier detection techniques the lack of flexibility of data analysis techniques such as acoustic analysis and motor current signal analysis (MCSA) in detecting a wide range of faults that could occur within the system. More recently, machine learning (ML) has been heavily utilised in literature for the modelling of such systems. As well as being a soft sensing technique, ML is able to provide a higher degree of flexibility in terms of application as well as being generally easier to implement than the aforementioned techniques. Specifically, Neural Networks (NNs) have been identified as an effective tool in data analysis and fault detection due to their unique ability to be trained to identify numerical relationships in different forms of data [8]. They provide an advantage over traditional signal processing and statistical techniques due to the level of complexity that they can model the data, as well as being generalizable to similar types of data. Various examples of literature can be found that utilise proposed NN models in multiple use cases and datasets [9; 10]. However, this is not to say that generalisation is still not an issue with NNs. The main issue found in literature with NNs is the importance of data volume and representation in being able to train NNs effectively. Most NN types such as the CNN and the LSTM require large quantities of data to effectively learn the shape and features of the data, and some methods even require the labelling of the data before training, known as supervised learning [11], which is very time-consuming and costly as this is usually a manual process. Furthermore, with some types of data it is very difficult to distinguish faults and anomalies in raw sequential format. To address these discussed issues with NNs, researchers have opted to combine signal processing techniques with NNs where applicable in order to utilise the advantages provided by the former with data representation and the latter in flexibility and ease of use.