## 2 State-of-the-Art Review  
As highlighted earlier, ensuring the safety and reliability of crane operations is of the utmost importance. Time series data from such heavy machinery operations pose unique challenges due to their large size and complex patterns. Promptly identifying operational errors can reduce accidents and downtime [23]. Machine learning and deep learning models have emerged as powerful tools for detecting anomalies in such data. Extensive literature exists on fault and failure detection in ML [24]. However, very little is available on error detection and prediction. It is crucial to detect and predict an error because an error is a precursor to faults and failure. Figure 2 below illustrates this correlation between error, defect, and failure. A machine's operational error signal detection refers to the process of identifying abnormal or unexpected signals in a machine's operation. These signals can indicate the system's or machine's problems, malfunctions, or deviations from normal behaviour. This research used actual on-board and off-board STS crane databases from ports and cranes, such as warranty, alarms, and operational data where error signals generated by the crane PLC were available. The significance of using deep learning for error detection in container crane operations lies in its ability to effectively capture complex patterns, temporal dependencies, and nonlinear relationships inherent in operational data. The vital points highlighting its significance are as follows:  
Figure 1: Research paper workflow. 1. Complexity of Operational Data: Container crane operations generate vast amounts of data, including sensor readings, crane movements, cargo handling, and environmental conditions. Deep learning algorithms, such as RNNs and CNNs, excel at processing high-dimensional and sequential data [18], making them well-suited to analysing the intricate dynamics of crane operations. 2. Temporal Dependencies: Crane operations exhibit temporal dependencies, where actions and events at one time step influence subsequent states and outcomes. RNNs, particularly LSTM networks, are designed to capture and learn from temporal dependencies in sequential data, enabling accurate modelling of the temporal evolution of crane operations and the detection of abnormal patterns or deviations [26; 27]. 3. Feature Learning: Deep learning models can automatically learn relevant features from raw data, eliminating the need for manual feature engineering. In the context of error detection in container crane operations, deep learning algorithms can extract informative features from sensor data, crane trajectories, and operational logs, enabling more effective detection of anomalies and deviations indicative of errors. 4. Adaptability to Complex Environments: container crane operations are subject to various environmental factors, such as wind, weather conditions, and sea states, which can influence crane performance and introduce uncertainties [28; 29]. Deep learning models, with their capacity to learn from diverse and noisy data sources, exhibit robustness and adaptability to complex operating environments, enhancing the reliability and accuracy of error detection systems [22]. 5. Scalability and Generalisation: Deep learning algorithms are inherently scalable and can handle large volumes of data, making them suitable for analysing the extensive datasets generated by container terminal operations. Moreover, deep learning models can generalise unseen data well, enabling effective error detection across different terminal configurations, crane types, and operating conditions [30]. 6. Continuous Learning and Improvement: Deep learning models support continuous learning and improvement through iterative training on new data. As additional operational data become available, deep learning-based error detection systems can be updated and refined to adapt to evolving operational conditions, improving their effectiveness over time [16; 31].