The state of health (SOH) estimation lithium-ion batteries is essential to ensure the safety electric vehicles. Electrochemical impedance spectroscopy (EIS) measurement can provide valuable ageing information and pave way for battery SOH estimation. However, semicircle's overlapping in EIS during degradation introduces large uncertainty electrochemical process, causing difficulty identifying features. Therefore, this paper proposes a hybrid intelligent model extraction highly influential...

The traditional on-house sensing (OHS) accelerometer for vibration measurements causes poor signal-to-noise ratio (SNR) and complicated fault modulations, which increases the difficulty complexity early bearing diagnosis. To overcome these challenges, this paper develops a wireless triaxial on-rotor (ORS) system to largely improve SNR deduces fast Fourier transform (FFT) Hilbert envelope analysis accurate rolling diagnosis, improves accuracy efficiency First, development of ORS is given....

With the rapid development of clean energy technologies, lithium-ion batteries have emerged as dominant power source. It is great significance to monitor state charge (SOC) and health (SOH) accurately efficiently for ensuing high safety reliability. This paper proposes an active acoustic emission (AE) sensing technology demonstrates feasibility co-estimation SOC SOH battery. The proposed method aims achieve a fast monitoring by putting insights into variation material properties during...

With the dramatic increase in electric vehicles (EVs) globally, demand for lithium-ion batteries has grown dramatically, resulting many being retired future. Developing a rapid and robust capacity estimation method is challenging work to recognize battery ageing level on service provide regroup strategy of retied secondary use. There are still limitations current methods, such as direct internal resistance (DCIR) electrochemical impedance spectroscopy (EIS), terms efficiency robustness. To...

The accurate lithium-ion battery capacity estimation is vital for ensuring the safe and reliable operation of battery-powered systems. Existing data-driven methods heavily rely on fixed charging stages feature extractions, posing significant limitations in real-world applications. This paper proposes an adaptable approach utilising short-duration random voltages during constant-current stage leveraging convolutional neural networks (CNNs). Based user-friendly "Vstart−tend" strategy, two...

Induction motors (IMs) play an essential role in the field of various industrial applications. Long-time service and tough working situations make IMs become prone to a broken rotor bar (BRB) that is one major causes faults. Hence, continuous condition monitoring BRB faults demands computationally efficient accurate signal diagnosis technique. The advantage high reliability wide applicability fault based on vibration signature analysis results improved cyclic modulation spectrum (CMS), which...

Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, overloads mean that it is subjected to broken rotor bar (BRB) faults. vibration signal of IMs with BRB faults consists the reliable modulation information for fault diagnosis. Cyclostationary analysis has been found be effective identifying extracting feature. estimators cyclic spectrum (CMS) fast spectral correlation (FSC) based on short-time...

Lithium-ion battery capacity estimation is crucial to ensure the operational reliability and safety of electric vehicles. Electrochemical Impedance Spectroscopy (EIS) can provide rich physical degradation information battery, which makes EIS-based data-driven method a promising solution for accurate estimation. However, batteries tend present diverse patterns due operating conditions manufacturing, resulting in large errors practical applications. Therefore, this paper proposes transfer...

The state of health (SOH) estimation lithium-ion batteries is crucial for the operational reliability and safety electric vehicles. However, traditional data-driven methods face problems label shortage domain discrepancy caused by diverse battery types operating conditions. This paper proposes a label-free SOH method based on adversarial multi-domain adaptation technique relaxation voltage (RV). Firstly, raw RV integral are proposed to construct two-dimensional input sequence ensure high...

Broken rotor bar (BRB) faults are one of the most common in induction motors (IM). One or more broken bars can reduce performance and efficiency IM hence waste electrical power decrease reliability whole mechanical system. This paper proposes an effective fault diagnosis method using Teager–Kaiser energy operator (TKEO) for BRB detection based on motor current signal analysis (MCSA). The TKEO is investigated applied to remove main supply component accurate feature extraction, especially...

Lithium-ion batteries have widely used as the power sources of electric vehicles (EVs). Accurate and rapid state health (SOH) estimation in battery management system (BMS) plays an essential part improving reliability safety systems. This paper develops active acoustic emission (AE) sensing technology for nonintrusive SOH estimation. The proposed method takes consideration into changing internal material properties under different levels degradation. In this method, ultrasound is to...

Lithium-ion battery state of health (SOH) estimation remains a significant challenge in management systems due to the sophisticated electrochemical processes within battery. As model-free method, data-driven-based method has shown great potential SOH estimation. However, existing data-driven approach requires large dataset and shows low model adaptability among different samples. To address issues, this paper proposes transfer learning (TL)-based technique coupled with multi-layer perceptron...

Abstract Accurate state of health (SOH) estimation lithium-ion batteries is great importance to ensure the reliability and safety battery management systems (BMS). The difficulty modelling complex degradation mechanism has made data-driven methods gain much attention in SOH prediction. To improve accuracy SOH, extracting suitable indicators still a challenging work. In this work, indication features are attracted from charging voltage profile based on experimental data measured under...

Forests promote the conservation of biodiversity and also play a crucial role in safeguarding environment against erosion, landslides, climate change. However, illegal logging remains significant threat worldwide, necessitating development automatic detection systems forests. This paper proposes use long-range, low-powered, smart Internet Things (IoT) nodes to enhance forest monitoring capabilities. The research framework involves developing IoT devices for sound classification transmitting...