The diagnosis of faults in rotating machinery is essential for maintaining reliability and operational efficiency. Shaft misalignment and bearing unbalance represent two of the most prevalent defects in such systems. This study investigates the effectiveness of a model-driven approach compared with machine learning (ML) techniques for the automatic detection of these faults, using vibration features extracted from vibration signals as diagnostic inputs. The model-driven method is based on a Bayesian framework (Acoem Accurex), while the ML approaches include a fully connected neural network and extreme gradient boosting (XGBoost). Experimental results indicate that XGBoost achieves the highest accuracy (70%) in identifying unbalance, outperforming the neural network (66%) and the Bayesian model (58%). For misalignment detection, however, the three methods exhibit comparable performance, underscoring the limitations of ML models that rely solely on vibration indicators for this fault type.
Switched reluctance motors (SRMs) are attracting increasing attention due to their operational advantages and their industrial use is rapidly growing. In parallel with advancements in motor technologies, there have also been significant developments in semiconductor technologies, control systems and condition monitoring. Programmable logic controllers (PLCs), which play a key role in these areas, are increasingly used in industrial motor control and data acquisition processes due to their modularity, reliability and compatibility with modern human-machine interface (HMI) tools. This study focuses on the design and implementation of a real-time laboratory platform for monitoring the electrical and mechanical behaviour of an 8/6 SRM using an advanced PLC. The proposed system visualises key parameters such as phase current and torque both locally through a HMI dashboard and remotely via a web interface. In addition to real-time monitoring, the system also performs data archiving during the condition monitoring process, enabling the storage and retrospective analysis of operational data. This configuration provides accessibility, transparency and flexibility in both academic and industrial settings. The study enables both local and web-based remote condition monitoring of SRM data, along with systematic data archiving, using an advanced PLC, which contributes to the literature and offers potential benefits in the field of industrial maintenance.
As a key component in mechanical equipment, the operating status of rolling bearings directly affects the stability and performance of the entire system. Existing mainstream fault diagnosis methods using vibration signals are primarily based on 'black box' models. Although they are characterised by high recognition accuracy, the decision-making basis and interpretability are insufficient, making it challenging to meet the needs of high-reliability application scenarios. Therefore, an interpretable Morlet convolutional neural network (MorletConv CNN) model based on Morlet convolution is proposed in this paper to reveal the inherent mechanism by which the model identifies fault features. It is difficult to extract features adaptively with traditional signal processing methods. Furthermore, the decision logic of convolutional neural networks (CNNs) is opaque. As a result, a Morlet preprocessing layer, embedded into the CNN structure, is designed in this paper. Combined with prior knowledge of time-frequency analysis, the proposed layer enables the model to learn key structural features in vibration signals more effectively, improving the accuracy and reliability of fault recognition. Specifically, the proposed model utilises two convolutional kernel branches, one for the real part and one for the imaginary part, to enhance sensitivity to minor signal changes and improve its ability to detect early faults. Simultaneously, the control parameter theta of the Morlet kernel function is set as a trainable variable to reduce model complexity and accelerate training. This function automatically aligns key frequency components during the optimisation process, improving the time-frequency feature extraction ability of the model. The validation results on multiple typical bearing fault datasets show that the proposed MorletConv CNN model is characterised by enhanced physical interpretability and generalisation ability while maintaining high diagnostic accuracy, providing new ideas and method support for achieving highly reliable rolling bearing fault diagnosis.
In the present paper, a novel multi-criterion-based adaptive neuro-fuzzy inference system (ANFIS) is proposed to determine the overall health index (OHI) of power transformer insulation. The proposed model concentrates on 12 diagnostic attributes related to dissolved gases, oil and paper insulation. On-field data from 150 transformers collected from Himachal Pradesh State Electricity Board, India, are utilised. Initially, the collected data are normalised and correlation among the attributes is established using a multi-criterion approach (MCA) according to IEEE standards C57.104-2019 and C57.106-2015. Closely related attributes are grouped into three grades. The obtained three grades are applied as input to the ANFIS model and the transformer health index (HI) is output. Among the collected data, 80% are used for training and 20% are used for testing. The performance of the proposed model is evaluated using two crucial error metrics: root mean square error (RMSE) and correlation coefficient (R 2 ). Moreover, the proposed model is validated using 110 data samples collected from the literature and compared with existing expert models, achieving an accuracy of 98.18%. The integration of the MCA with the ANFIS model overcomes the shortcomings of the previous ANFIS model, requiring huge training time, larger rule formation and a high computational burden on the network. The proposed MCA-based ANFIS model is easy to implement and accurately predicts the health indices. The present work is beneficial for diagnostic experts looking to take appropriate remedial actions on the current health status of transformer insulation.
ABSTRACT The INCOSE future of systems engineering (FuSE) initiative is a collaborative effort to realize the Systems Engineering Vision 2035 , pivoting from traditional, process‐driven methods to a more agile approach working with the different engineering and systems engineering disciplines, It focuses on addressing complex societal challenges through holistic, sustainable, and AI‐enabled systems engineering, creating actionable roadmaps. The The Agile Systems and Systems Engineering working group has identified eight strategic aspects, two of which are the following: ▪ Shared‐knowledge management – facilitated communication, collaboration, and knowledge curation ▪ Adaptable modular architectures – composable and reconfigurable designs from evolving variations of reusable assets Model‐based systems engineering (MBSE) has progressed to the point that it is now ubiquitous in systems engineering. It is therefore logical that MBSE be used together with systems engineering agility in order to leverage its benefits. This article will examine how the OMG Reusable Asset Specification (RAS), the Modular Open Systems Approach (MOSA), product line engineering (PLE) used as part of MBSE, and agile can address these strategic aspects to enable systems engineering agility.
