Development of an Explainable Fault Diagnosis Framework Based on Sensor Data Imagification: A Case Study of the Robotic Spot-Welding Process . Jiho Lee In-Bai Noh Jihyun Lee Sang Won Lee. Engineering, Computer Science. IEEE Transactions on Industrial Informatics. 2022; TLDR. An explainable fault diagnosis framework that infers the causal …
However, on large-scale automatic production lines, on-site uncertainties such as material inhomogeneity, residual impurities, and parameter fluctuations increase the welding instability and easily lead to welding defects, which will seriously affect the quality and performance of power batteries [ 3, 4 ].
Conclusion For the diagnosis of sensor faults in batteries, an amalgamation of the battery equivalent circuit model and a data-driven approach is deployed. In the diagnosis of faults related to battery voltage and current sensors, a model-centric methodology is employed.
The battery sensor failure may lead to the failure of monitoring the battery state, thus affecting the effective management of battery safety and performance. Battery sensor failure occurs when a single type of sensor is abnormal and does not affect other sensors, and may also return to normal after a period of time.
At present, most of the post-welding quality evaluation of power batteries is mainly carried out by manual visual inspection, which is bound to cause low detection efficiency and high labor costs, making it difficult to meet the requirements of modern welding production for high efficiency and high quality.
A power battery is one of the key components of new energy vehicles, and its quality determines the reliability and safety of the vehicle to a large extent. Laser welding is widely used in power battery manufacturing due to its advantages of high energy density, high precision, and precise control over the heat input [ 1, 2 ].
However, the welding defects in the BCC during the welding process are characterized by a disorganized distribution, extensive size variations, multiple types, and ambiguous features, posing challenges for detecting welding defects in the current collector.
Development of an Explainable Fault Diagnosis Framework Based on Sensor Data Imagification: A Case Study of the Robotic Spot-Welding Process . Jiho Lee In-Bai Noh Jihyun Lee Sang Won Lee. Engineering, Computer Science. IEEE Transactions on Industrial Informatics. 2022; TLDR. An explainable fault diagnosis framework that infers the causal …
Laser welding is widely used in lithium-ion batteries and manufacturing companies due to its high energy density and capability to join different materials. Welding quality plays a vital role in the durability and effectiveness of welding structures. Therefore, it is essential to monitor welding defects to ensure welds quality. Manual inspection, analysis and evaluation …
The automatic detection of laser welding quality in power batteries is crucial for ensuring the safety performance of new energy vehicles. This paper proposes a framework that combines deep network and conventional image processing techniques to achieve efficient and accurate detection of laser welding quality.
In this study, we present a novel collection of 3,736 laser welding images which are labeled with eight classes. This dataset contains both normal and defective classes collected from a Dade Laser Chinese production line. Moreover, we introduce a modified loss function that integrates cross entropy and complement objective training.
This research paper proposes a novel approach for real-time defect detection during laser welding of battery cells by analyzing the acoustic and spectral process emissions. The main objective is to explore the correlations between the acoustic and spectral signatures and the related weld defects, enabling an accurate evaluation of ...
The acquisition of sensor data from the battery holds paramount importance for the seamless functioning of new energy vehicles. Therefore, the real-time identification of …
Electric vehicles'' batteries, referred to as Battery Packs (BPs), are composed of interconnected battery cells and modules. The utilisation of different materials, configurations, and welding processes forms a plethora of …
Uncovering subtle battery behavior changes for improved fault detection. Specific focus on multidimensional signals to enhance safety strategies. Future trends in battery fault diagnosis driven by AI and multidimensional data.
The acquisition of sensor data from the battery holds paramount importance for the seamless functioning of new energy vehicles. Therefore, the real-time identification of faults in battery sensors becomes imperative to proactively prevent more severe lithium-ion battery failures. A proposed approach for typical fault diagnosis of ...
Applications in New Energy Vehicle Manufacturing. In the field of new energy vehicle (NEV) manufacturing, ultrasonic metal welding machines play a key role in ensuring the integrity and performance of electrical systems. This technology is utilized in a variety of applications during electrical system assembly, including: Busbar Welding
This research paper proposes a novel approach for real-time defect detection during laser welding of battery cells by analyzing the acoustic and spectral process emissions. The main objective is to explore the …
This article proposes a lightweight deep-learning algorithm called MGNet for detecting welding defects in the current collectors. We introduce a lightweight MDM module …
Battery surface reconstruction can inspect the quality of the weld instead of relying on human inspection. This paper proposes a defect detection method in the small field of view based on...
In this study, we present a novel collection of 3,736 laser welding images which are labeled with eight classes. This dataset contains both normal and defective classes collected from a Dade Laser Chinese production line. Moreover, we introduce a modified loss function …
The automatic detection of laser welding quality in power batteries is crucial for ensuring the safety performance of new energy vehicles. This paper proposes a framework …
Failure assessment in lithium-ion battery packs in electric vehicles using the failure modes and effects analysis (FMEA) approach
Lanbao sensors are widely used in PV equipment, such as PV silicon wafer manufacturing equipment, inspection / testing equipment and lithium battery production equipment, such as winding machine, laminating machine, coating machine, series welding machine, etc., to provide lean testing solution for new energy equipment.
Reliable quality control of laser welding on power batteries is an important issue due to random interference in the production process. In this paper, a quality inspection framework based on a...
PDF | Ultrasonic metal welding (USMW) is a common used manufacturing technology for cell, module or pack assembly of Lithium-ion battery systems of... | Find, read and cite all the research you ...
Uncovering subtle battery behavior changes for improved fault detection. Specific focus on multidimensional signals to enhance safety strategies. Future trends in …
Battery applications often join metals that can be challenging to weld. Copper, aluminum, and nickel are commonly used in battery construction, and while welding a material to itself is easy, welding dissimilar combinations, such as copper to nickel, can be problematic.. Copper. A wonderful electrical conductor, copper is often at the center of many battery designs, used in …
Welding defects on new energy batteries based on 2D pre-processing and improved-region-growth method in the small field of view . October 2023; Measurement Science and Technology 35(1) DOI:10.1088 ...
Reliable quality control of laser welding on power batteries is an important issue due to random interference in the production process. In this paper, a quality inspection framework based on a...
Battery surface reconstruction can inspect the quality of the weld instead of relying on human inspection. This paper proposes a defect detection method in the small field of view based on 2D pre-processing and an improved-region-growth method.
The invention discloses a new energy battery pole welding seam defect detection method, equipment and a storage medium, wherein the method comprises the following steps: step 1, …
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