## 2 State-of-the-Art Review
Key performance indicators were developed to assess ship-to-shore crane performance and provide insights for future improvements in container crane equipment operation. The findings suggest that the performance indicators can guide future inspections and improvements in container crane operations. Promising progress is being made in using deep learning for fault identification and prediction in time series data. To tackle problems like noisy labelling, ensemble methods for fault identification [45], and innovative models like GRU-enhanced DCNN for better fault diagnosis in chemical processes [46], several strategies have been put forth. Furthermore, deep learning models for anomaly identification in spacecraft telemetry channels have been investigated, highlighting the necessity of automated methods for effectively monitoring system anomalies [47]. These studies demonstrate how deep learning methods, such as recurrent units and convolutional neural networks, can improve fault detection efficiency, generalisability, and accuracy in various industrial areas. Deep learning has shown remarkable capabilities in analysing complex data patterns and making predictions. Deep neural networks (DNNs) excel in learning representationsfrom large-scale data, enabling them to detect subtle signals indicative of impending errors in crane operations. Techniques such as RNNs and CNNs have been adapted to model sequential and spatial dependencies in crane sensor data, operation logs, and environmental factors. Key deep learning architectures that may be used for the Boolean time series data from the STS container cranes include the following [18; 48; 49; 50]:  
* Recurrent Neural Networks (RNNs): RNNs are adept at handling sequential data, rendering them suitable for time-series analysis. RNNs can effectively capture temporal dependencies in crane operation data [51]. * Long Short-Term Memory (LSTM): LSTM networks, a variant of RNNs, are designed to address the vanishing gradient problem. LSTMs are particularly adept at modelling long-term dependencies and have been successfully utilised to predict crane behaviour. * Gated Recurrent Unit (GRU): GRUs are another RNN variant that maintains memory while being computationally efficient. GRUs have shown promise in predicting input force values for crane control. RNNs are a robust machine learning technique that excels at handling sequential data, such as time series and natural language processing. RNNs contain a feedback loop that allows them to learn complicated correlations in input sequences and exploit these structures in a nonlinear fashion [52]. RNNs have been extensively employed in diverse domains, such as sentiment analysis, energy demand forecasting, and monthly flow prediction [53]. An essential benefit of RNNs is their capability to handle input sequences of varying lengths and produce output sequences of varying lengths. These characteristics make them suitable for tasks such as language modelling, machine translation, and speech recognition [51]. Deep neural networks, which are capable of learning even more intricate representations of the input data, can be created by stacking RNNs [54]. More sophisticated RNN architectures, such as LSTMs and GRUs, have been created to solve the problem of long-term dependencies, which can be problematic for regular RNNs. These designs better capture long-term dependencies in the input data by employing gating techniques to retain and forget information selectively. In the available literature, the methodologies used for Boolean time series error or fault detection, diagnosis, and prediction in manufacturing industries based on machine learning can be seen in Table 1 below, and deep learning can be seen in Table 2 below, as follows:  
Container cranes are crucial in container terminals and are fundamental to the global economy because most of the world's goods pass through them. However, their automation level is lower than those of yard cranes and automated guided vehicles (AGVs) [21].