Premature battery drain, swelling and fires/explosions in lithium-ion batteries have caused wide-scale customer concerns, product recalls, and huge financial losses in a wide range of products including smartphones, laptops, e-cigarettes, hoverboards, cars, and commercial aircraft. Most of these problems are caused by defects which are difficult to detect using conventional nondestructive electrical methods and disassembly-based destructive analysis. This paper develops an effective computed tomography (CT)-based nondestructive approach to assess battery quality and identify manufacturing-induced defects and structural deformations in batteries. Several unique case studies from commercial e-cigarette and smartphone applications are presented to show where CT analysis methods work.

To effectively extract early fault features in rotating machinery signals contaminated by intense noise, this paper proposes a novel fault detection method based on feature mode decomposition (FMD) and a maximum correlated kurtosis combined L-Kurtosis deconvolution algorithm (MCK-LKD). The method innovatively introduces a combined kurtosis indicator named CK-LK and establishes the MCK-LKD algorithm by integrating the statistical characteristics of correlated kurtosis and L-Kurtosis, significantly improving the detection capability for fault impact components. Furthermore, a two-stage processing framework FMD-MCK-LKD is constructed, effectively combining the noise reduction advantages of FMD with the fault enhancement capabilities of the MCK-LKD algorithm. The proposed method is tested on both simulated and experimental signals, with comparative analysis conducted against other mainstream deconvolution algorithms. The results demonstrate that the signal decomposition and reconstruction method based on FMD, combined with the MCK-LKD algorithm, exhibits significant advantages and superior performance in early fault detection of rotating machinery.

As a critical component of rotating machinery, bearings directly affect the operational safety of the system. However, in real industrial scenarios, the imbalanced distribution of fault samples impairs the accuracy of diagnostic systems. Existing fault diagnosis methods still suffer from limited representation capability in fusing high-dimensional heterogeneous features with fault information, while language model-based diagnostic approaches face challenges such as insufficient interpretability. To address these issues, this paper proposes a frequency-aware multi-view feature fusion fault diagnosis framework, termed frequency-aware and multi-view fusion bearing fault diagnosis framework (FMFDF). First, raw vibration signals are converted into statistical semantic prompts and time–frequency spectrograms to construct multi-view inputs consisting of text and images. Then, a fault frequency feature extraction network (FF-FEN) is designed to embed FF information into the feature extraction process, where frequency-constrained convolutions enhance the feature representation of FF bands. Subsequently, a multi-head attention mechanism is employed to align and fuse the textual semantic features with the frequency-aware time–frequency features, followed by fault classification using a LoRA-fine-tuned BERT and classification network. The proposed method is validated on the case western reserve university and Jiangnan university bearing datasets. Experimental results demonstrate that FMFDF achieves favourable diagnostic stability under imbalanced sample conditions and offers a certain degree of interpretability.

Analog fault diagnosis is a field of paramount importance, and test signal generation is an important prerequisite for analog fault detection. Several stimuli have been used as input test vectors. This study presents a novel approach for the adoption of classical methods and signals for fault detection. This involves the use of asymmetrical periodic signals and comparison of their effectivity in maximizing output response between faulty and conforming circuits. The technique helps to determine a minimal set of test signals for a circuit. It also enables the test designer to identify the parts of the frequency spectra of various signal types that can pose problems of fault masking and fault dominance. In addition, the technique indicates certain sets of components forming ambiguity groups, which exhibit complementary fault-masking effects. The method does not require access to internal nodes of the circuit. It only requires generation of standard asymmetrical signals and hence can be implemented with the use of commonly available function generators. The technique is applicable to integrated circuits and printed circuit boards, as well as analog subsystems. It can also be applied for fault isolation. The results include responses from representative benchmark analog circuits.

Heterogeneous gear fault vibration signals are often influenced by the coupling of multi-level modulation sources, which severely hinders the separation of informative features and obstructs accurate tracking of their intrinsic temporal evolution. Moreover, current research in fault diagnosis lacks effective co-modeling strategies that can jointly characterize the multi-source coupling mechanisms and their dynamic progression, making it difficult to decouple fault-relevant patterns from interfering components in such modulated signals.To address this issue, we propose a physics-guided spatiotemporal decoupling framework, which integrates spatiotemporal modeling with the underlying physical modulation mechanisms. First, inspired by the kinematic characteristics of gear systems, a Heuristic Selective Decision Solver (HSDS) is designed to extract modulation patterns dominated by rotational periodicity, thereby achieving effective isolation of multi-source interference. Next, the extracted patterns are transformed into physically meaningful enhanced representations via a spatiotemporal encoder, which simultaneously preserves the local structural details and temporal evolution of fault features. Furthermore, a Spatiotemporal Coupling Capture Network (SCCN) is developed, incorporating a physically salient attention mechanism to adaptively emphasize critical fault-related components, significantly improving the discriminative capability of the learned features. Finally, a neural operator-based classifier is employed to accomplish robust fault recognition. Experimental results demonstrate that the proposed method achieves high diagnostic accuracy and robustness across a variety of complex operating conditions, enabling efficient and reliable fault diagnosis for gear transmission systems.