Industry 5.0, also known as the “smart factory”, is an evolution of manufacturing technology that utilizes advanced data analytics and machine learning techniques to optimize production processes. One key aspect of Industry 5.0 is using vibration data to monitor and detect anomalies in machinery and equipment. In the case of a vertical carousel storage and retrieval system (VCSRS), vibration data can be collected and analyzed to identify potential issues with the system’s operation. A correlation coefficient model was used to detect anomalies accurately in the vertical carousel system to ascertain the optimal sensor placement position. This model utilized the Fisher information matrix (FIM) and effective independence (EFI) methods to optimize the sensor placement for maximum accuracy and reliability. An LSTM-autoencoder (long short-term memory) model was used for training and testing further to enhance the accuracy of the anomaly detection process. This machine-learning technique allowed for detecting patterns and trends in the vibration data that may not have been evident using traditional methods. The combination of the correlation coefficient model and the LSTM-autoencoder resulted in an accuracy rate of 97.70% for detecting anomalies in the vertical carousel system.

The service life of aluminium electrolytic capacitors is becoming a critical design factor in power supplies. Despite rising power density demands, electrolytic capacitors and switching devices are the two most common parts of the power supply that age (deteriorate) under normal and diverse working conditions. This study presents a fault diagnostics framework integrated with long-term frequency for a switched-mode power supply aluminium electrolytic capacitor (SMPS-AEC). Long-term frequency condition monitoring (CM) was achieved using the advanced HIOKI LCR meter at 8 MHz. The data acquired during the experimental study can help to achieve the needed paradigm from various measured characteristics of the SMPS/power converter component to detect anomalies between the capacitors selected for analysis. The CM procedure in this study was bound by the electrical parameters—capacitance (Cs), equivalent series resistance (ESR), dissipation factor (DF), and impedance (Z)—-acting as degradation techniques during physical and chemical changes of the capacitors. Furthermore, the proposed methodology was carried out using statistical feature extraction and filter-based correlation for feature selection, followed by training, testing and validation using the selected supervised learning algorithms. The resulting assessment revealed that with increased data capacity, an improved performance was achieved across the chosen algorithms out of which the k-nearest neighbors (KNN) had the best average accuracy (98.40%) and lowest computational cost (0.31 s) across all the electrical parameters. Further assessment was carried out using the fault visualization aided by principal component analysis (PCA) to validate and decide on the best electrical parameters for the CM technique.

The role of transformers in power distribution is crucial, as their reliable operation is essential for maintaining the electrical grid’s stability. Single-phase transformers are highly versatile, making them suitable for various applications requiring precise voltage control and isolation. In this study, we investigated the fault diagnosis of a 1 kVA single-phase transformer core subjected to induced faults. Our diagnostic approach involved using a combination of advanced signal processing techniques, such as the fast Fourier transform (FFT) and Hilbert transform (HT), to analyze the current signals. Our analysis aimed to differentiate and characterize the unique signatures associated with each fault type, utilizing statistical feature selection based on the Pearson correlation and a machine learning classifier. Our results showed significant improvements in all metrics for the classifier models, particularly the k-nearest neighbor (KNN) algorithm, with 83.89% accuracy and a computational cost of 0.2963 s. For future studies, our focus will be on using deep learning models to improve the effectiveness of the proposed method.

Speed reducers (SR) and electric motors are crucial in modern manufacturing, especially within adhesive coating equipment. The electric motor mainly transforms electrical power into mechanical force to propel most machinery. Conversely, speed reducers are vital elements that control the speed and torque of rotating machinery, ensuring optimal performance and efficiency. Interestingly, variations in chamber temperatures of adhesive coating machines and the use of specific adhesives can lead to defects in chains and jigs, causing possible breakdowns in the speed reducer and its surrounding components. This study introduces novel deep-learning autoencoder models to enhance production efficiency by presenting a comparative assessment for anomaly detection that would enable precise and predictive insights by modeling complex temporal relationships in the vibration data. The data acquisition framework facilitated adherence to data governance principles by maintaining data quality and consistency, data storage and processing operations, and aligning with data management standards. The study here would capture the attention of practitioners involved in data-centric processes, industrial engineering, and advanced manufacturing techniques.

Recent research has seen an interest in the condition monitoring (CM) approach for aluminium electrolytic capacitors (AEC), which are present in switched-mode power supplies and other power electronics equipment. From various literature reviews conducted and from a failure mode effect analysis (FMEA) standpoint, the most critical and prone to fault component with the highest percentage is mostly capacitors. Due to its long-lasting ability (endurance), CM offers a better paradigm for AEC due to its application. However, owing to severe conditions (over-voltage, mechanical stress, high temperature) that could occur during use, they (capacitors) could be exposed to early breakdown and overall shutdown of the SMPS. This study considered accelerated life testing (electrical stress and long-term frequency testing) for the component due to its endurance in thousands of hours. We have set up the experiment test bench to monitor the critical electrical parameters: dissipation factor (D), equivalent series resistance (ESR), quality factor (Q), and impedance (Z), which would serve as a health indicator (HI) for the evaluation of the AECs. Time-domain features were extracted from the measured data, and the best features were selected using the correlation-based technique.

The ability of a switch-mode AC/DC power supply to shrink supplies is a benefit and a requirement for most electronic devices with limited space. Major failures in switch-mode power supply (SMPS) during adverse working conditions are subject to mostly the switching devices and capacitors. For effective condition monitoring of the SMPS, dual (or multiple) sensing provides a more reliable standpoint against the traditional single sensing techniques as it provides a more comprehensive paradigm for accurate condition monitoring. This study proposes an integrated approach to SMPS condition monitoring by exploiting statistically extracted features from current and voltage signals for system fault diagnosis based on electrical stress. Following a correlation-based feature selection approach, salient features were utilized for improved fault detection and isolation (FDI) using ML-based classifiers. Diagnostic results by the classifiers reveal that the random forest and gradient boosting classifiers are highly reliable but computationally expensive when compared with the others while the decision tree was quite cost-efficient with reliable diagnostic results. The proposed framework is effectively applicable for use in diagnosing the switching devices and classification at different states.