In association rule mining, frequent itemset mining (FIM) optimization is moving from software to hardware acceleration. High utility itemset mining (HUIM), which is an advanced FIM extension, solves traditional FIM's inability to handle highvalue data well. However, the hardware acceleration of HUIM is more complex and memory-intensive, demanding stricter FPGAbased acceleration design, leaving FPGA-accelerated HUIM research preliminary. To address this, this paper proposes an FPGA coprocessor for HUIM acceleration. It includes a multiplexed computation unit that enhances join operation efficiency and is scalable to parallel modules, and a prefix-free itemset method that reduces redundant computation and access via hardware traits. Experimental results demonstrate that the proposed CPUFPGA heterogeneous parallel processing architecture achieves a speedup of up to$5 \times$compared to pure software-based HUIM methods

This study provides a comprehensive overview of challenges and advancements in vibration analysis for machinery operations under non-stationary and non-linear conditions. Non-stationary operation in machinery occurs when operating conditions such as speed, load, and environmental factors change over time. This results in dynamic behaviours that cause fluctuating vibration signals, making fault detection challenging with traditional methods that assume stationary conditions. The paper provides foundational insights and clear concepts on essential topics, including non-stationary operations in rotary machinery , vibration signals in non-stationary operations, cycle-stationary analysis, and the quantification of non-stationary operations. Further advancing, this paper explores the challenges and methodologies in condition-based monitoring for non-stationary machinery operations, focusing on the analysis of vibrational signals. It examines the complexities of working with non-stationary and cyclo -stationary signals and the limitations of traditional signal processing techniques . The study reviews classical time–frequency and advanced signal-processing methods, highlighting their advantages, drawbacks, and applicability in real-world scenarios. Additionally, it addresses the identification of defects across varying operational speeds, identifying gaps in current methodologies and suggesting potential avenues for future research. The paper also emphasizes the importance of transfer learning in non-stationary environments, analyzing various approaches and their effectiveness in improving monitoring performance. Lastly, it discusses the development of expertise and adoption pathways for AI-based predictive maintenance , offering insights into the practical integration of advanced technologies in industrial settings.

Bearing fault diagnosis is critical for ensuring mechanical reliability and operational safety. Industrial Internet of Things (IIoT) sensors provide real-time monitoring data, advancing research in data-driven approaches to bearing fault diagnosis. However, current studies overlook two key challenges: 1) susceptibility to noise interference during fault signal acquisition and 2) the scarcity of fault data for effective diagnostic tasks in practical scenarios. To address these issues, this article proposes a novel method termed convolutional denoising autoencoder and siamese neural network (CDAE-SNN) for fault diagnosis in rolling bearings. This method is designed to be robust against noise and applicable in scenarios with limited data. Initially, Gaussian white noise is added to raw signals to simulate noisy signals encountered in real operating conditions. Subsequently, a convolutional denoising autoencoder (DAE) is constructed and optimized. The encoder in CDAE compresses feature information from samples into a lower dimensional space, while the decoder reconstructs signals to mitigate noise effects. Denoised signal sample pairs are then fed into a 2-D convolutional neural network-based siamese network to generate embedding vectors. Fault classification of rolling bearings is performed based on similarity metrics between sample pairs. Experimental results confirm the enhanced diagnostic accuracy of our proposed model across various signal-to-noise ratios and sample sizes. Furthermore, the model exhibits superior performance in classifying faults across diverse proportion of new categories.

Prognostics and health management (PHM) has garnered significant attention in industrial fields, particularly due to its successful application in managing battery degradation. However, current approaches are inadequate in addressing multiple thresholds, including both theoretical formulation and practical computational complexity. These limitations hinder the development and implementation of threshold-varying assessments, thereby impeding the advancement of PHM application. This article investigates prognostic applications with different failure thresholds and highlights the importance of failure threshold selection. In addition, theoretical evaluation and analysis are provided for multiple threshold settings, encompassing both discrete and continuous series. This introduces a novel technical domain for prognostic applications. The effectiveness of threshold-varying assessment is verified with several different approaches on real battery degradation experiments. Furthermore, we demonstrate the practical significance of threshold-varying assessments in enabling on-demand scheduling for maintenance or replacement of spare parts. Most importantly, to meet the real-time requirements of practical prognostic applications, this article also discusses the computational complexity of threshold-varying assessment and finds an applicable solution for this common difficulty.

This paper presents a novel data-driven predictive maintenance scheduling framework for aircraft engines based on remaining useful life (RUL) prediction. First, a deep learning ensemble model is proposed to effectively predict aircraft engine RUL, including a one-dimensional convolutional neural network (CNN) and a bidirectional long short-term memory network with an attention mechanism (Bi-LSTM-AM). Second, we propose a Bayesian optimization method to optimize the hyperparameters in the deep learning ensemble model to further improve RUL prediction performance. As the aircraft engine RUL decreases over time and eventually triggers a maintenance alarm threshold. The maintenance scheduling task is initiated after the aircraft engine maintenance alert threshold has been triggered. To effectively implement the maintenance scheduling plan, we develop a novel and effective mixed-integer linear programming (MILP) model to cope with aircraft engine maintenance scheduling, which aims to minimize the maximum maintenance time. Finally, experimental results show that our proposed data-driven predictive maintenance scheduling framework can monitor the running status of aircraft engines in real time and reduce their maintenance time.

The existing fault diagnosis methods of rotating machinery constructed with both shallow learning and deep learning models are mostly based on vibration analysis under steady rotating speed. However, the rotating speed frequently changes to meet practical engineering needs. The shallow learning models largely depend on domain experience of feature extraction, and training a deep learning model requires large samples and a long time. In addition, vibration monitoring has the shortcomings of contact measurement, small coverage, and noise interference. To address these problems, this article proposes a new fault diagnosis method with the least square interactive support matrix machine (LSISMM) and infrared thermal images. In this method, a novel matrix-form classifier called LSISMM is constructed under the concept of nonparallel interactive hyperplanes to fully leverage the structure information of infrared thermal images. To improve the computation efficiency, a new least square loss constraint is designed for LSISMM. Besides, we derive an effective solution framework based on the alternating direction method of the multiplier (ADMM) framework. The constructed LSISMM is directly used to analyze the collected thermal images of rotating machinery under time-varying speeds. Experiment results demonstrate that the proposed method is superior to state-of-the-art methods in terms of diagnosis accuracy and efficiency, especially under small thermal image samples.

Complete fault sample is essential to activate artificial intelligent (AI) models. A novel fault detection scheme is proposed to build a bridge between AI and real-world running mechanical systems. First, the finite element method simulation is used to simulate samples with different faults to overcome the shortcoming of missing fault samples. Second, to enlarge datasets, new samples similar to the simulation and measurement fault samples are generated by generative adversarial networks and further combined with the original simulation and measurement samples to obtain synthetic samples. Finally, the synthetic and unknown fault samples are severed as the training and test samples, respectively, to the classifiers of AI models, and the unknown fault types will be finally determined. A public datasets of bearings have been used to verify the effectiveness of the proposed scheme. It is expected that the proposed scheme can be extended to complex mechanical systems.