The electrical connector is an essential component of all kinds of electronic equipment. It is important to conduct the remaining useful life (RUL) prediction of electrical connectors. The intermittent fault phenomenon has been observed during the degradation process of electrical connectors. This paper proposes combining contact resistance features and intermittent faults to predict the RUL of electrical connectors. The evolution characteristics of the contact resistance and intermittent faults in the whole-life degradation process of electrical connectors are compared and analyzed. It is found that the precursor of intermittent fault feature comes out earlier than the rise of contact resistance, which is used to determine the first predicting time (FPT) for RUL prediction. The RUL prediction of electrical connectors is carried out by combining the time-domain evolution features of the contact resistance and intermittent faults, based on a long short-term memory network with an attention mechanism (attention-LSTM). Experimental results demonstrate that the proposed method has good accuracy for the RUL prediction of electrical connectors.

The intermittent fault in electrical systems is considered to be one of the most challenging problems to tackle. At present, there remains a lack of understanding as to the mechanism of intermittent fault. Therefore, this paper is focused on the characteristics of intermittent fault in electrical connectors under vibration environment. Based on the mechanical structure of a certain kind of electrical connector, a dynamic model is constructed to analyze how its contact resistance varies under vibration environment. In addition, a vibration test rig intended for electrical connectors and a contact resistance acquisition system are developed. Under vibration environment, experiments are conducted on electrical connectors with intermittent fault injected. According to the results obtained from both sweep-frequency and fixed-frequency vibration test, some conclusions are summarized regarding the frequency, amplitude and phase characteristics of the intermittent fault in electrical connectors, which is conducive to improving the understanding as to the fundamental mechanism of intermittent fault caused by the application of vibration stress.

Intermittent faults are very common problems in various kinds of electronic devices. This paper is focused on the intermittent fault phenomenon in degraded electrical connectors in vibration environments. To analyze the intermittent faults in degraded electrical connectors, a micro-state contact model is constructed to simulate the resistance variation behavior in vibration environments. The relationship between the intermittent fault behavior and the degradation state is studied, and a health indicator parameter is extracted from the signals to act as a symptom for degradation. A test platform is built to collect the intermittent fault signals from electrical connectors at different degradation states in vibration environments. The degradation curve of the health indicator is used to predict the remaining useful life (RUL) of the electrical connectors. The method of extreme learning machine (ELM) is adopted to conduct the prediction of RUL. The genetic algorithm (GA) is used to optimize the weight and bias parameters in ELM. The results reflect that the intermittent fault characteristic is a proper degradation index in the prediction of RUL for degraded electrical connectors.

Intermittent faults are widely present in aviation electronic devices, especially in various electrical connectors. It is usually hard to diagnose the source of the intermittent faults, which brings a huge challenge to the repair and maintenance of equipment. This paper focuses on the intermittent faults in typical aviation electrical connectors activated by shock test. The transient arc wave is observed on a nanosecond scale during the occurrence of the intermittent faults. An arc signal model is constructed to analyze the impact factors of the signal. Based on the arc wave characteristics, further intermittent fault diagnostic analyses are conducted on four types of connector components: damaged solder joints, cracked pin connections, loose wire connections and worn electrical connectors. The effective arc wave components of the raw signals are extracted using Variational Mode Decomposition (VMD), and a comparison is made between traditional diagnostic method and CNN-based deep learning method. The results show that the combination of VMD-CNN-SVM achieves the optimal diagnostic effect. The diagnostic results reflect that the proposed arc signal features are suitable for diagnosing intermittent faults in connector components.

Focusing on the challenges posed by the strong randomness, weak manifestation, and difficulty in diagnosing intermittent faults (IFs) in high-density integrated circuits (HDICs)—often induced by bonding wire defects—this paper takes the GPIO interfaces of a typical DSP chip as the research object. It systematically analyzes the response characteristics of intermittent short-circuit and open-circuit faults and proposes a hybrid intelligent diagnosis method based on the Sparrow Search Algorithm-optimized Variational Mode Decomposition and Attention-based Support Vector Machine (SSA–VMD–Attention–SVM). A dedicated fault injection circuit is designed to accurately replicate IFs and acquire the power supply current response signals. The Sparrow Search Algorithm (SSA) is employed to adaptively optimize the parameters of Variational Mode Decomposition (VMD) for effective extraction of frequency-domain features from fault signals. A three-level attention mechanism is introduced to adaptively weight multi-domain features, thereby highlighting the key fault components. Finally, the Support Vector Machine (SVM) is utilized to achieve high-precision fault classification under small-sample conditions. Experimental results demonstrate that the proposed method achieves a diagnostic accuracy of 97.78% for intermittent short-circuit and open-circuit faults in the GPIO interfaces of the DSP chip, significantly outperforming traditional methods and exhibiting notable advantages in terms of diagnostic accuracy, robustness, and interpretability.