Ball Defect Research Articles

Solder Ball Defect Detection in BGA-Packaged Chips

Solder Ball Defect Detection in BGA-Packaged Chips

Triboelectric nanogenerator-embedded intelligent bearing with rolling ball defect diagnosis via signal decomposition and automated machine learning

Smart fault diagnosis of bearings is of great significance due to their extensive applications on various occasions. Recently, self-powered sensing technology based on triboelectric nanogenerators promotes the development of intelligent bearings. However, the effective detection and recognition of the rolling element defects of bearings need to be investigated further. This study proposes a triboelectric sensor-embedded rolling bearing (T-bearing) to monitor the working conditions and conduct the defect diagnosis of rolling balls. The interdigitated copper electrode covered by polytetrafluoroethylene film is attached to the inner surface of the outer ring of a commercial bearing. Such a design not only directly forms the TENG with rolling balls to obtain the contact-sensing signals, but also successfully achieves the diagnosis of rolling ball defects with similar triboelectric signals through a novel analysis and prediction paradigm combining signal decomposition and automated machine learning. Finally, a recognition accuracy of 99.48% with five different conditions of bearing balls is reached, which is extremely superior to the highest accuracy of 78.34% without signal decomposition. Thus, this study provides a new strategy for the defect diagnosis and the intelligent application of triboelectric bearings.

Defect enhancement method for ZrO2-bearing spherical surface representation based on complete defect stitching and self-defined background balancing algorithm

To address the problems of difficulty in extracting complete defects and high interference of surface background in the process of ZrO2 bearing ball defect detection. A method of ZrO2 bearing ball surface representation defect enhancement based on complete defect stitching and self-defined background balance algorithm is proposed. To determine the location of complete defects, we propose a scanning and acquisition scheme for sub-aperture images and quickly stitch the edges of sub-aperture images. The image feature point gradient definition is optimized, and a less error Euclidean distance finding method is used for feature point matching. The stitched complete defect image is smooth and has high pixel accuracy. The background feature layer of the complete defect image is extracted for background gray leveling, and this process is introduced into the γ-value calculation process. The γ setting allows the image to be self-defined enhancement, outputting a fully defective image with a balanced background and outstanding defects. The average gradient is reduced by more than 90% after complete defect image enhancement, and the information entropy is preserved by about 40%. The defect edges of the image are intact, and the fine texture of the defect features is preserved.

Precision fault prediction in motor bearings with feature selection and deep learning

In the disciplines of industrial machinery, mechanical engineering is beneficial to recognize motor performance for motors with HP power, torque transducer, dynamometer, and control electronics. The motivation is to address the need for more accurate and efficient fault prediction in machinery to prevent breakdowns, reduce maintenance costs, and improve overall reliability. In this work, deep learning classifiers used to classify ball defect inner race fault, outer race fault and normal motor performance in testing. With the aid of three distinct classifiers CNN, FFNN, and RBN; these suggested relative characteristics are assessed. In comparison to other current algorithms, the suggested methodology for classifying motor performance achieved maximum accuracy in each CNN test at 95.4% and 97.7%. The correlation and chi-square algorithms are used to find out the added characteristics and rank of features. The correlation technique provides relations between attributes, and the chi-square offers the optimal balance between precision and feature space. We discovered that the performance is enhanced overall by relative power characteristics. The suggested models might offer rapid responses with less complexity.

Ranked feature-based data-driven bearing fault diagnosis using support vector machine and artificial neural network

This article analyzes a data-driven fault diagnosis method for rolling element bearing under different operating conditions. The work is aimed to incorporate the filter-type feature selection algorithms, namely, neighborhood component analysis, minimum redundancy maximum relevance, chi-square, and ReliefF algorithms for the feature ranking of the extracted time-domain statistical features from the collected vibration data of the five different bearing defect conditions (normal, outer race defect, ball defect, inner race defect, and combined inner and ball defect). The vibration data have been recorded and an envelope analysis technique for pre-processing the signal is used to identify the fundamental defect frequencies of all the bearings. The artificial neural network and the support vector machine are used to predict the comparative performance of the feature selection algorithms. The results demonstrate that the neighborhood component analysis algorithm achieves the highest accuracies for the bearing fault detection with both the support vector machine and artificial neural network among all the feature selection methods. Therefore, the effective feature ranking before the fault classification by the machine learning algorithms can lead to an efficient and reliable bearing fault diagnosis.

Deep Transfer Learning Framework for Bearing Fault Detection in Motors

The domain of fault detection has seen tremendous growth in recent years. Because of the growing demand for uninterrupted operations in different sectors, prognostics and health management (PHM) is a key enabling technology to achieve this target. Bearings are an essential component of a motor. The PHM of bearing is crucial for uninterrupted operation. Conventional artificial intelligence techniques require feature extraction and selection for fault detection. This process often restricts the performance of such approaches. Deep learning enables autonomous feature extraction and selection. Given the advantages of deep learning, this article presents a transfer learning–based method for bearing fault detection. The pretrained ResNetV2 model is used as a base model to develop an effective fault detection strategy for bearing faults. The different bearing faults, including the outer race fault, inner race fault, and ball defect, are included in developing an effective fault detection model. The necessity for manual feature extraction and selection has been reduced by the proposed method. Additionally, a straightforward 1D to 2D data conversion has been suggested, altogether eliminating the requirement for manual feature extraction and selection. Different performance metrics are estimated to confirm the efficacy of the proposed strategy, and the results show that the proposed technique effectively detected bearing faults.

Performance of Bearing Ball Defect Classification Based on the Fusion of Selected Statistical Features.

Among the existing bearing faults, ball ones are known to be the most difficult to detect and classify. In this work, we propose a diagnosis methodology for these incipient faults’ classification using time series of vibration signals and their decomposition. Firstly, the vibration signals were decomposed using empirical mode decomposition (EMD). Time series of intrinsic mode functions (IMFs) were then obtained. Through analysing the energy content and the components’ sensitivity to the operating point variation, only the most relevant IMFs were retained. Secondly, a statistical analysis based on statistical moments and the Kullback–Leibler divergence (KLD) was computed allowing the extraction of the most relevant and sensitive features for the fault information. Thirdly, these features were used as inputs for the statistical clustering techniques to perform the classification. In the framework of this paper, the efficiency of several family of techniques were investigated and compared including linear, kernel-based nonlinear, systematic deterministic tree-based, and probabilistic techniques. The methodology’s performance was evaluated through the training accuracy rate (TrA), testing accuracy rate (TsA), training time (Trt) and testing time (Tst). The diagnosis methodology has been applied to the Case Western Reserve University (CWRU) dataset. Using our proposed method, the initial EMD decomposition into eighteen IMFs was reduced to four and the most relevant features identified via the IMFs’ variance and the KLD were extracted. Classification results showed that the linear classifiers were inefficient, and that kernel or data-mining classifiers achieved classification rates through the feature fusion. For comparison purposes, our proposed method demonstrated a certain superiority over the multiscale permutation entropy. Finally, the results also showed that the training and testing times for all the classifiers were lower than 2 s, and 0.2 s, respectively, and thus compatible with real-time applications.

Fault Classification of Rolling Element Bearing in Machine Learning Domain

Rolling element bearings are crucial components of rotating machinery used in various industries, including aerospace, navigation, machine tools, etc. Therefore, it is essential to establish suitable techniques for condition monitoring and fault diagnosis of bearings to avoid breakdowns and damages during operation for overall industrial sustainability. Vibration-based condition monitoring has been the most employed technique in this perspective. Many researchers have investigated the vibration response of rolling element bearings having inner race defects, outer race defects, or rolling element defects using conventional techniques in past decades. However, Machine Learning (ML) has emerged as another way of bearing fault diagnosis. In this work, fault signatures of ball bearings are classified using a total of 6 (with 24 subcategories) ML models, and a comparative performance of these models is presented. The ML classifiers are trained with extracted time-domain and frequency-domain features using open-source Case Western Reserve University (CWRU) bearing data. Two datasets of different sample size and number of samples of vibration data corresponding to a healthy ball bearing, a defective bearing with inner race defect, a ball defect, and an outer race defect, running at a particular set of working conditions, are considered. The accuracy of ML models is compared to identify the best model for classifying the faults under consideration.

Classification of Induction Motor Bearing Failures Through Retro-Propagation Neural Network Algorithm and Adaptive Neuro-Fuzzy Inference System of Type Takagi-Sugeno

The goal of this work was to study the best technique for fault diagnosis in bearing induction motors. Degraded operating modes may occur during the life of the induction motors. One of the main causes of these failures is the defects of the bearings. To improve the operational safety of the drives, monitoring facilities can be placed to perform preventive maintenance. We present a classification of the vibration vector signal based on the vibration data obtained from the vector signal for four types of bearing defects (healthy, ball defect, inner ring and outer ring defect). The automatic diagnosis of these vectors is performed using artificial intelligence techniques that combine retro-propagation neural network algorithm and fuzzy inference system adaptive network of type Takagi-Sugeno. These techniques give accurate results that are confirmed by numerical simulation.

Assembly Wirebond Process Solution for Mitigating Leadframe Bouncing on Multi-Die QFN Device

Modification and improvement of an existing tooling design in semiconductor packaging industry has been a usual practice, to enhance the current setup and to provide a solution to a specific assembly problem. This paper discusses the solution in eliminating the smashed ball defect occurrence observed after wirebond process. Smashed ball is usually encountered if the unit is unstable and creates a bouncing effect during wirebond process. It is therefore important to mitigate this micro-bouncing effect by analyzing the package design and the window clamp and top plate (WCTP). The objective is to increase the stability of the unit during wirebonding, especially for quad-flat no-leads (QFN) package with no tape. To achieve this, the solution is to alter the vacuum hole design of the top plate from single hole per unit to multiple holes of varied sizes per unit. Ultimately, after changing the design of the top plate, the micro-bouncing encountered during wirebond process was significantly reduced. This in turn created a consistent ball formation in all bonded wires. The comparative data presented in this paper confirmed the effectivity of the redesigned WCTP. For future works and studies, the improvement and learnings could be used on devices with comparable configuration.

FBGA solder ball defect effect on DDR4 data signal rise time and ISI measured by loading the data line with a capacitor

This paper proposes a new method of investigating the effect of void or fracture in FBGA solder ball on the DDR4 data signal rise time and inter-symbol interference (ISI), by loading the data line with a capacitor. A void or fracture in solder ball increases its capacitance which effects the data signal rise time and increases ISI. For measuring ISI large consecutive patterns of 1's or 0's followed by a changing bit are used. However in in-field systems it is not possible to run large patterns of 1's or 0's. So the data line is loaded with a 0.2 pF capacitive load on a UDIMM test card to mimic the increased capacitance due to FBGA solder ball void defect of height 0.2 mm and cross sectional area of 0.0045 mm2. The loaded line shows increase in rise time of 16 ps. For loaded line the data eye opening is 0.077 UI lesser. This decrease in data eye means more ISI and will cause increase in intermittent errors.

Elimination of Smashed Ball Defect through Process and Design Improvement

This paper presents the modification and improvement done on the wire clamp and top plate (WCTP) design to eliminate the bouncing effect of the silicon die that leads to smashed ball reject during the formation of wire. The protrusion of the unit on the vacuum hole produced movement and slight vibration that affects the consistency of wirebonding. Through changing the standard vacuum hole to micro-holes provides underneath support to the unit and eliminates the overhanging or protrusion during the formation of wire. The implementation of the micro-hole design reduced the defect parts per million (dppm) occurrence to zero.

Damping characteristics of integral squeeze film dampers on vibration of deep groove ball bearing with localized defects

Purpose This work aims to demonstrate the vibration suppression of the rotor system with localized defects on bearing using an integral squeeze film damper (ISFD). Design/methodology/approach Experiments were carried out to study the vibration characteristics of the rotor system with ISFD mounted on fault deep groove ball bearings. Three fault bearings including bearing with outer race defect, inner race defect and ball defect have been used in this paper. The results were compared by use of vibration acceleration level, continuous wavelet transform and envelope spectrum. Findings It was found that ISFD shows excellent damping and vibration attenuation characteristics of the rotor system with defective bearing. The fault bearing rotor system with external ISFD considerably reduces the vibration energy and amplitude compared with the system without ISFD. Originality/value There is a dearth of experimental research pertaining to vibration characteristics of rotor system support by defective bearings with ISFD. Besides, the test provides evidence for the application of ISFD in vibration control of the rotor system with incipient defects on bearing. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2020-0144/

Identification of Bearing Faults using Wavelet Transform

This research is concerned with description of a scheme for bearing’s localized defect detection based on wavelet packet transform (WPT). WPT provides a high resolution time-frequency distribution from which periodic structural ringing due to repetitive force impulses, generated upon the passing of each rolling element on the defect, are detected. The objective of this work is to emphasis on the outer race defect, inner race defect and ball defect. In modern industrial scenario, there is increasing demand for automatic condition monitoring that reduce the gap between digital model and actual product. With reliable condition monitoring, faults such as machine element failures could be identified in their early-stages and further damage to the system could be prevented. Successful monitoring is a complex and application-specific problem, but a generic tool would be useful in preliminary analysis of new signals and in verification of known theories.

DIAGNOSA KERUSAKAN BANTALAN BOLA MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

<p><em>Bearings are the critical part of any rotating machine. The catastrophic failure of the bearing can lead to fatal and harmful to the operation of the machine. Therefore, predictive maintenance based on condition monitoring of bearing is very important. The objective of this research is to apply Support Vector Machine (SVM) method for fault diagnosis of the ball bearing. The research was carried out at the bearing test rig. Four types of ball bearing condition, such as normal, inner race defect, ball defect, and outer race defect were measured of the vibration signals using data acquisition with a sampling frequency of 20 kHz at the constant speed of 1400 RPM. Various features were extracted from vibration signals in time domain, such as RMS, variance, standard deviation, crest factor, shape factor, skewness, kurtosis, log energy entropy and sure entropy. PCA transformation was employed to reduce the dimension of feature extracted data. SVM classification problems were solved using MATLAB 2016a. The results showed that the application of RBF kernel function with the C parameter =1 was the best configuration. The training model accuracy was 98.93% and the testing accuracy of SVM was 97.5%. Finally, the research results show that the SVM classification method can be used to diagnose the fault condition of the ball bearing.</em><em>.</em></p>

Condition Monitoring of Two Stroke IC Engine Ball Bearing Based on Vibration Signals Using Machine Learning Techniques

Ball bearing faults are foremost cause to the breakdown of rotating machine components. In general, ball bearing in the IC engine is one of the vital machine elements and plays major role in the rotating machine as it takes different kinds of stresses and dynamic loads under various working conditions. Monitoring of such ball bearing continuously is extremely needed to avoid damage to the components of IC Engine. Vibration analysis of IC engine is most commonly used to recognise the fault in ball bearing and to differentiate the types of ball bearing defects. In the present work, study has been conducted on condition monitoring of ball bearing of 2-stroke single cylinder IC engine using vibration signals and explains the use of statistical features and support vector machine as a tool for classification of the ball bearing conditions. The different cases of faults considered in the diagnosis of ball bearing are outer race (OR) defect, inner race (IR) defect, combined faults in outer and inner race (OR & IR) and ball defect. The vibration signals under good and different simulated defective conditions of ball bearing are collected using an accelerometer. The descriptive statistical set of features are found from the collected vibration signals. The significant features are carefully chosen by means of J48 decision tree technique and the features chosen from tree are given as input to the support vector machine (SVM). The experimental results show that, the SVM model provides 96% classification accuracy for the given vibration signals. The obtained result from the model is good and can be considered for diagnosis of rotating machinery. Hence, the J48 decision tree and SVM algorithm can be proposed for applications of condition monitoring of ball bearings.

Study of noise effect on bearing vibration signal based on statistical parameters

The signals emanating from the bearings are complex and contribute to various distributions. The effect of the distribution and mathematical operations are responsible for the change in the statistical moments. This paper investigates the effect of noise on statistical moments of the bearing vibration signals. Initially, the distribution function for Healthy, inner race defect (IRD), outer race defect (ORD), and ball defect (BD) are tested using Kolmogorov Smirnov test (K-S test). The resulting distributions obtained from the K-S test are normal and Laplacian distributed patterns and convey the faulty state of the bearings. The change in noise levels and their influence on the statistical moments are verified. It is observed, the kurtosis for IRD and ORD decreases with increase in noise, whereas, the trend increases for healthy and BD faults.

A Novel Coarse–Fine Method for Ball Grid Array Component Positioning and Defect Inspection

With high production efficiency and robustness, automatic component pick-and-place technology is widely used in modern electronic industries. As one of the core components of this technology, the visual measurement system requires a suitable positioning and fault detection algorithm with high speed, accuracy, robustness, and generalization abilities, especially for positioning and inspecting ball grid array (BGA) components. This paper examines the online positioning and defect inspection problem of BGA components for component placement machines. Incorporating coarse and fine positioning, an accurate, efficient, and robust universal positioning algorithm is proposed. Two types of key points are introduced to characterize the alignment of solder balls in BGA components. A distance transform-based circle detection method is first applied, and then a distance-based edge filter and a circle fitting method are employed to obtain the coarse and fine locations of solder balls, respectively. The approximate location of the component is estimated using a geometrical method and the fine location is calculated by solving a least-squares problem. A pair of overlapping ratios is introduced to conduct fault detection and inspect the alignment accuracy. The effectiveness of the proposed method is verified by applying it to several real component positioning and defect inspection experiments.

Analysis of Effective Area of Steel Ball Defect Detection Based on Image

Analysis of Effective Area of Steel Ball Defect Detection Based on Image

Dynamic modeling and vibration response simulations of angular contact ball bearings with ball defects considering the three-dimensional motion of balls

A dynamic model to investigate the dynamics and vibration responses of angular contact ball bearings with ball defects is proposed. In the proposed model, each ball has 6 degrees of freedom (DOFs), meaning that the three-dimensional motion of balls is considered. The results show that whether a ball hit a race and the frequency property of the envelope spectrum largely depend on the geometrical characteristics and operating conditions. This increases the difficulty in diagnosing the ball defect when using vibration-based monitoring techniques. The current investigation suggests that the vibration response simulations should be carried out to investigate the vibration mechanism and to diagnose the ball defect more effectively.