Untimely machinery breakdown will incur significant losses, especially to the manufacturing company as it affects the production rates. During operation, machines generate vibrations and there are unwanted vibrations that will disrupt the machine system, which results in faults such as imbalance, wear, and misalignment. Thus, vibration analysis has become an effective method to monitor the health and performance of the machine. The vibration signatures of the machines contain important information regarding the machine condition such as the source of failure and its severity. Operators are also provided with an early warning for scheduled maintenance. Numerous approaches for analyzing the vibration data of machinery have been proposed over the years, and each approach has its characteristics, advantages, and disadvantages. This manuscript presents a systematic review of up‐to‐date vibration analysis for machine monitoring and diagnosis. It involves data acquisition (instrument applied such as analyzer and sensors), feature extraction, and fault recognition techniques using artificial intelligence (AI). Several research questions (RQs) are aimed to be answered in this manuscript. A combination of time domain statistical features and deep learning approaches is expected to be widely applied in the future, where fault features can be automatically extracted from the raw vibration signals. The presence of various sensors and communication devices in the emerging smart machines will present a new and huge challenge in vibration monitoring and diagnosing.

The term Internet of Things (IoT) has emerged in recent decades because this network revolutionizes almost every aspect of our daily life, including products such as smartphones and intelligent vehicles, and crucial tasks such as precision agriculture and environmental monitoring. Myriads of communication technologies have been developed to fulfill the two main features of the IoT: long-range transmissions and low power consumption. Long-range (LoRa) has become one of the vital parts of IoT communication. In this study, the real-time deployments of an unmanned aerial vehicle (UAV)-based LoRa communication network are systematically reviewed, with a focus on the communication setup and its reported performance. Importantly, the UAV-based LoRa communication network has a low bit rate connectivity to ensure the high reliability of connections, especially in applications that require long transmission ranges. This study provides recommendations for researchers on what research perspectives need to be explored when implementing UAVs for IoT-based LoRa communication. This study also describes publication trends related to UAV-based LoRa communication networks. A supplementary Excel file that contains the reported publications on UAV-based LoRa communication networks is included to show this publication trend.

Recent years have seen a huge increase in the study of drones. There is a lot of published articles regarding drone, focusing on control optimization, fault detection, safety mechanisms, etc. In fault detection, most studies focused on the effects of faulty propellers and rotors, and there is very limited academic research on drone arms. In this paper, a fault detection based on the vibration of the multirotor arms using artificial intelligence (AI) is proposed. There are some cases in which, due to accident, the arm of the multirotor crack or loosen. This is normally unnoticeable without disassembly, and if not taken care of, it would have likely resulted in a sudden loss of flight stability, which will lead to a crash. Different types of AI methods are incorporated in this study, namely, fuzzy logic, neuro-fuzzy, and neural network (NN). Their results are compared to determine the best method in predicting the safety of the multirotor. Fuzzy logic and neuro-fuzzy methods provided acceptable decision-making, but the performance of the neuro-fuzzy approach depend on the dataset used because overfit model might give incorrect decision-making. This also applies to the NN technique. Because the vibration data are collected in the laboratory environment without consideration of wind effect, this framework is more suitable for early prediction before flying the multirotor in the outdoor environment.

Early drone anomaly inspection is vital to ensure the drone’s safety and effectiveness. This process is often overlooked, especially by amateur drone pilots; however, some faulty conditions are difficult to notice by the naked eye or discover, even though the drone inspection process has been conducted; therefore, a real-time early drone inspection approach based on vibration data is proposed in this study. Firstly, the reliability of several microelectromechanical systems (MEMS) sensors, namely the ADXL335 accelerometer, ADXL 345 accelerometer, ADXL377 accelerometer, and SW420 vibration sensor in detecting faulty conditions, were tested and compared. The experimental results demonstrated that the vibration parameter measured using ADXL335 and ADXL345 accelerometers are the best choice as most of the faulty conditions can be detected, in contrast to other MEMS sensors. The output produced from the anomaly inspection algorithm is then converted to the “Healthy” or “Faulty” state, which is displayed in a mobile application for easy monitoring.

Unmanned aerial vehicles (UAVs) or UAVs are increasingly applied by military and civilians in a wide range of applications, such as environmental monitoring, search and rescue, target detection, combat, precision agriculture, and three-dimensional (3-D) mapping. The failure of UAVs can cause human casualties and property damage. Hence, it is crucial to consistently monitor the UAV in order to detect any potential malfunction in vital components like motors and electronic speed controllers (ESCs). This study proposed a new UAV condition-monitoring approach that combines sensor fusion and fuzzy-based decision-making algorithms. The vibration, the motor's rotational speed, and current parameters are utilized to determine the UAV's condition, specifically the UAV's motors or ESCs. The UAV's condition is categorized into safe, partial safe, and danger. Experimental results show a clear distinction between a healthy and faulty UAV's component in terms of vibration, with a discrepancy of more than 100% in the majority of the cases. The proposed framework can successfully alert the user if there is a malfunction in the UAV's motor or ESC before or when the UAV is flying.

Path planning in robotics and automation often demands solutions that effectively balance efficiency and path smoothness, particularly in diverse and large-scale environments. To address this, this paper presents the Rapidly Exploring Random Tree-Modified Whale Optimization Algorithm II (RRT-MWOAII), an enhanced version of the previous Rapidly Exploring Random Tree-Modified Whale Optimization Algorithm (RRT-MWOA) approach. The RRT-MWOAII is a hybrid technique that combines the strengths of the RRT path planning method and the MWOAII. It utilizes the RRT path as the initial solution and then applies the MWOAII to improve the smoothness of the RRT path. The key advancement in the RRT-MWOAII algorithm is the incorporation of a targeted global search, where the weakest solution is targeted instead of a random one during the global search phase. This targeted approach helps to efficiently identify and refine the weakest link in the solution, leading to improved overall path optimization. Additionally, the algorithm further refines its local solution by employing a neighborhood search, which allows for fine-tuning of the path. Simulations were conducted and evaluated on seven benchmarks, including RRT, RRT*, Bidirectional RRT (BiRRT), and state-of-the-art optimization methods such as the original Whale Optimization Algorithm (WOA), Improved Whale Optimization Algorithm (IWOA) and Sparrow Search Algorithm (SpSA). Experimental results show that MWOAII consistently produced shorter and smoother paths, outperforming its predecessor with a 31.6% improvement in the initial RRT path smoothness and a 21.05% better average smooth cost across all nine maps.