The collection and analysis of data play a critical role in detecting and diagnosing faults in bearings. However, the availability of large open-access rolling-element bearing datasets for fault diagnosis is limited. To overcome this challenge, the University of Ottawa Rolling-element Bearing Vibration and Acoustic Fault Signature Datasets Operating under Constant Load and Speed Conditions are introduced to provide supplementary data that can be combined or merged with existing bearing datasets to increase the amount of data available to researchers. This data utilizes various sensors such as an accelerometer, a microphone, a load cell, a hall effect sensor, and thermocouples to gather quality data on bearing health. By incorporating vibration and acoustic signals, the datasets enable both traditional and machine learning-based approaches for rolling-element bearing fault diagnosis. Furthermore, this dataset offers valuable insights into the accelerated deterioration of bearing life under constant loads, making it an invaluable resource for research in this domain. Ultimately, these datasets deliver high quality data for the detection and diagnosis of faults in rolling-element bearings, thereby holding significant implications for machinery operation and maintenance.

The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, the future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis are highlighted.

Bearings are a key component of rotating machines, and their fault diagnosis is critical for safe operation of rotating machines. Since bearings often work under variable speed conditions and their vibrations contain rich information of health conditions, time–frequency analysis (TFA) of vibration signals has been shown to be an effective way to perform bearing fault diagnosis. However, applications of traditional TFA methods for analyzing vibrations from bearings are often constrained by limited time variability and smearing effects. This article proposes an instantaneous frequency (IF) synchronized-generalized stepwise demodulation transform (IFS-GSDT) method for TFA of nonstationary vibration signals. Demodulators of the proposed IFS-GSDT method are first derived as functions of inclined angles formed by IF lines of windowed signals; thus, IF preestimation is no longer required. A spectral kurtosis-guided strategy is then developed to determine optimal inclined angles. To effectively tackle multicomponent signals, the proposed IFS-GSDT method explores a new linear transforming kernel that synchronizes the demodulators to all signal components, and an iteration procedure can be avoided. The proposed method also allows for the signal to be reconstructed when the window length under analysis is fixed. The effectiveness of the proposed method is validated using simulations and measured vibration data. Comparisons between the proposed method and other popular TFA methods are also conducted to demonstrate the superior characteristics of the proposed method.

The experimental study of vibrating plates having simply-supported boundary conditions can be difficult to achieve due to the complexity of preventing translation, but allowing rotation along all boundaries simultaneously. Only a few methods have been proposed, but all are either time-consuming to set up and involve customization of the test rig for each plate or do not allow the plate to be reused for other purposes. The method described in this paper offers a low-cost, simple, accurate and non-destructive way of experimentally measuring the modal properties of thin, simply supported plates and can be used for quick validations of models and designs without modification for multiple trials and varying plate properties. The key attributes of this method include: •An adjustable sliding support frame which can be made of a distinct material from the plate and which can accommodate variations in plate geometry and properties without modification.•Removable flexible sealant applied in a v-groove on the supporting frame which can be easily used to fix and support the plate according to the simply-supported boundary conditions.•A low-profile design, which can be used to accommodate most experimental testing methods for determining modal properties of vibrating plates.