Fishing wealth is one of the richest resources in the Sultanate of Oman. It is considered as one of the most important economic developments that nation depends on in a larger way. The Sultanate of Oman is characterized by the presence of a large fishing fleet as the number of fishing vessels and boats in it. Good research with the application of modern technology in fishermen boats is required to increase the quality of fishing by providing fishermen with a safe and secure fishing experience. Artificial intelligence (AI) in boat automation technology is new and it is a mandatory demand for Oman’s fisheries sector. At the time of fishing, there are a lot of problems fishermen face such as weather changes, border tracking, navigation, illegal fishing, pirate attack, oil spill, technical fault in boats, etc. Therefore, the application of AI and related techniques in boat automation, information sharing, and preparation of documentation resources is very important in this sector. The main requirement for a fisherman is a high-quality fishing boat with proper communication devices to provide all the required information to fishermen and the control room. In this paper, a review has been made on fishermen’s boats with artificial intelligence for a sustainable fishing experience ensuring safety, security, navigation, and sharing information for Omani fishermen.

Health management in industrial systems is crucial for maintenance management, and it plays an important role in productivity, fault diagnosis, safety, efficiency, and economy in manufacturing industries. Early detection of faults in machinery may increase the effectiveness of maintenance actions and will avoid unwanted consequences in process operations and maintenance. Existing fault diagnosis methods have limitations such as insufficient accuracy, slow detection rate, and handling large and complex data sets. In this digital age, Industry 4.0 techniques have been applied across all fields, including the condition monitoring of machines. This research addresses the gaps in traditional fault diagnosis by using deep learning, a modern AI technique effective for diagnosing faults in various machines. In this research work, vibration signals are collected using the National Instruments- Data Acquisition (NI-DAQ) system, accelerometer, and LabVIEW software. These signals are processed using a series of steps, including sampling strategy, shuffling, standardization, and reshaping data augmentation. Deep learning algorithms Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) are tested for fault diagnosis using vibration datasets collected from Spectra Quest Machinery Fault Simulator (SQMFS). The result shows that MLP accuracy in the fault prediction is 0.9, CNN reached 0.95, and RNN and LSTM with 0.57 and 0.45, respectively. The high performance of CNN is due to its ability to effectively capture spatial patterns in vibration data, which is crucial for fault diagnosis in rotating machinery, followed by MLP due to its faster convergence during training. When the data is scaled, MLP performs better than CNN, demonstrating its adaptability to increased data complexity and volume. RNN and LSTM resulted in lower accuracy due to the need for larger datasets and temporal patterns in the vibration data, which they are designed to handle. This study shows that CNN has given better results than other deep learning algorithms, such as MLP, RNN, and LSTM, in fault diagnosis of rotating machinery. Future research could explore applying these techniques to different types of machinery and fault conditions.

In the industrial sector, intelligent sensors in fault diagnosis are becoming more critical in recent technological improvements. The prediction accuracy can be enhanced in fault diagnosis using 3-dimensional, sequential, real-time, and image data. Sensors that capture the vibration, sound, and image data are more critical in predicting unbalancing, tool wear, crack, misalignment, etc, in the rotating machinery to increase productivity and to provide an effective maintenance management system. Due to the fast development of industry 4.0 techniques, monitoring of mechanical machinery is experiencing explosive growth and getting more attention in the area of Fault Diagnosis (FD). Machine learning and Deep learning methods give promising results and accuracy in predicting faults in rotating machinery on shop floors. The success of AI-based models is due to the availability of comprehensive labeled data. Federated Learning (FL) is the machine learning subfield aiming to train an algorithm with a heterogeneous dataset. Data transmission from local facilities to a central server in AI models creates data privacy and security issues. Heterogeneous data analysis is a complicated process in predicting the machine fault in the central server because of millions of data during real-time condition monitoring. Decentralized data handling and analysis is mandatory in condition monitoring because of heterogeneity in data processing, data privacy, and security advantages. The application of federated learning in fault diagnosis has been getting more attention in recent days, and this study is a review of FL applications that address fault diagnosis in rotating machinery in the first phase. A comparison between the types of FL approaches in FD, and the use of aggregation algorithms and their applications will also be discussed in Phase 1. In phase 2, a novel methodology has been proposed using Federated learning to diagnose rotating machinery faults. The proposed method, FLOACOS, addresses how prediction challenges are solved using federated learning approaches by optimizing and standardizing the data at local facilities. This work will be helpful for future condition monitoring researchers and gives an overview of a novel method of the FL technique used in predicting faults and the progress made in the maintenance management of rotating machinery.

Real-time condition monitoring and precision health assessment systems are necessary for effective maintenance programs in the industrial sector. Rapid advancements in information technology and other engineering technologies have invited more proactive attention from research and development in industrial sectors, particularly in condition monitoring of machines and related Industrial processes. In this work, drill bit condition monitoring techniques have been developed based on wavelet analysis and Artificial Neural Networks (ANN) for automatic drill bit fault detection and classification. An experimental work has been conducted to capture the vibration signals for analysis. The CNC drill machine uses a high-carbon steel drill bit and mild steel material as work pieces in this experiment. The cutting condition parameters are kept constant, and the wear level varies from 0.2 to 0.6 mm. Using an accelerometer, the Data Acquisition system (DAQ) with Lab VIEW software captures the vibration signals for drill bits with different wear conditions. The captured vibration data are analyzed using Continuous Wavelet Transform (CWT) with Morlet and Daubechies wavelet as prime functions. The CWT coefficient is generally used to generate the input features to ANN for automatic tool condition classification, with two outputs (0, 1) for healthy and (1, 0) for faulty. The outcome of ANN showed 98% accuracy in the wear prediction, and these results show the effectiveness of the combed WT and ANN for the automatic classification of tool wear conditions with a high success rate.