Fault Knowledge Graph Construction and Platform Development for Aircraft PHM

To tackle the problems of over-reliance on traditional experience, poor troubleshooting robustness, and slow response by maintenance personnel to changes in faults in the current aircraft health management field, this paper proposes the use of a knowledge graph. The knowledge graph represents troubleshooting in a new way. The aim of the knowledge graph is to improve the correlation between fault data by representing experience. The data source for this study consists of the flight control system manual and typical fault cases of a specific aircraft type. A knowledge graph construction approach is proposed to construct a fault knowledge graph for aircraft health management. Firstly, the data are classified using the ERNIE model-based method. Then, a joint entity relationship extraction model based on ERNIE-BiLSTM-CRF-TreeBiLSTM is introduced to improve entity relationship extraction accuracy and reduce the semantic complexity of the text from a linguistic perspective. Additionally, a knowledge graph platform for aircraft health management is developed. The platform includes modules for text classification, knowledge extraction, knowledge auditing, a Q&A system, and graph visualization. These modules improve the management of aircraft health data and provide a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis.

Proactive Fault Diagnosis of a Radiator: A Combination of Gaussian Mixture Model and LSTM Autoencoder

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of the Gaussian Mixture Model and Long-Short Term Memory autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states-two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of the Gaussian Mixture Model and Long Short-Term Memory in fault detection. Gaussian Mixture Models are deployed for initial fault classification, leveraging their clustering capabilities, while Long-Short Term Memory autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the Gaussian Mixture Model and the reconstruction error distribution of the Long-Short Term Memory autoencoder model.

Preventing Forklift Front-End Failures: Predicting the Weight Centers of Heavy Objects, Remaining Useful Life Prediction under Abnormal Conditions, and Failure Diagnosis Based on Alarm Rules

This paper addresses the critical challenge of preventing front-end failures in forklifts by addressing the center of gravity, accurate prediction of the remaining useful life (RUL), and efficient fault diagnosis through alarm rules. The study's significance lies in offering a comprehensive approach to enhancing forklift operational reliability. To achieve this goal, acceleration signals from the forklift's front-end were collected and processed. Time-domain statistical features were extracted from one-second windows, subsequently refined through an exponentially weighted moving average to mitigate noise. Data augmentation techniques, including AWGN and LSTM autoencoders, were employed. Based on the augmented data, random forest and lightGBM models were used to develop classification models for the weight centers of heavy objects carried by a forklift. Additionally, contextual diagnosis was performed by applying exponentially weighted moving averages to the classification probabilities of the machine learning models. The results indicated that the random forest achieved an accuracy of 0.9563, while lightGBM achieved an accuracy of 0.9566. The acceleration data were collected through experiments to predict forklift failure and RUL, particularly due to repeated forklift use when the centers of heavy objects carried by the forklift were skewed to the right. Time-domain statistical features of the acceleration signals were extracted and used as variables by applying a 20 s window. Subsequently, logistic regression and random forest models were employed to classify the failure stages of the forklifts. The F1 scores (macro) obtained were 0.9790 and 0.9220 for logistic regression and random forest, respectively. Moreover, random forest probabilities for each stage were combined and averaged to generate a degradation curve and determine the failure threshold. The coefficient of the exponential function was calculated using the least squares method on the degradation curve, and an RUL prediction model was developed to predict the failure point. Furthermore, the SHAP algorithm was utilized to identify significant features for classifying the stages. Fault diagnosis using alarm rules was conducted by establishing a threshold derived from the significant features within the normal stage.

Immunohistochemical expression of BCL-2 in hydatidiform moles: a tissue microarray study

Background

Hydatidiform moles (HM) are members of gestational trophoblastic diseases (GTD) and, in some cases, might progress to gestational trophoblastic neoplasia (GTN). HMs are either partial (PHM) or complete (CHM). Some HMs are challenging in arriving at a precise histopathological diagnosis. This study aims to investigate the expression of BCL-2 by immunohistochemistry (IHC) in HMs as well as in normal trophoblastic tissues "products of conception (POC) and placentas" using Tissue MicroArray (TMA) technique.

Methods

TMAs were constructed using the archival material of 237 HMs (95 PHM and 142 CHM) and 202 control normal trophoblastic tissues; POC and unremarkable placentas. Sections were immunohistochemically stained using antibodies against BCL-2. The staining was assessed semi-quantatively (intensity and percentage of the positive cells) in different cellular components (trophoblasts and stromal cells).

Results

BCL-2 showed cytoplasmic expression in more than 95% of trophoblasts of PHM, CHM and controls. The staining showed a significant reduction of the intensity from controls (73.7%), PHMs (76.3%) to CHM (26.9%). There was a statistically significant difference between PHM and CHM in the intensity (p-value 0.0005) and the overall scores (p-value 0.0005), but not the percentage score (p-value > 0.05). No significant difference was observed in the positivity of the villous stromal cells between the different groups. All cellular components were visible using the TMA model of two spots/case (3 mm diameter, each) in more than 90% of cases.

Conclusions

Decreased BCL-2 expression in CHM compared to PHM and normal trophoblasts indicates increased apoptosis and uncontrolled trophoblastic proliferation. Construction of TMA in duplicates using cores of 3 mm diameter can overcome tissue heterogeneity of complex lesions.

Hydatidiform mole

Hydatidiform mole is the most common form of gestational trophoblastic disease. It is an abnormally formed placental tissue with characteristic changes in karyotype, arising in fertilization disorders. The presence of abundant paternal genetic information plays a key role in the pathogenesis of complete and partial hydatidiform moles. These lesions are characterized by a relatively wide spectrum of morphological changes that may not be fully expressed, especially in the early stages of pregnancy. In addition, some changes can be observed in non-molar gravidities, which, unlike hydatidiform moles, lack any risk of malignant transformation. Although conventional histological examination still plays a key role in the diagnosis, it should be supplemented by other methods that reliably differentiate individual lesions. Accurate diagnosis of molar gravidities is important not only for determining the correct therapeutic approach, but the obtained data may also contribute to further research of these pathological entities.

Rolling Bearing Fault Diagnosis Using Hybrid Neural Network with Principal Component Analysis

With the rapid development of fault prognostics and health management (PHM) technology, more and more deep learning algorithms have been applied to the intelligent fault diagnosis of rolling bearings, and although all of them can achieve over 90% diagnostic accuracy, the generality and robustness of the models cannot be truly verified under complex extreme variable loading conditions. In this study, an end-to-end rolling bearing fault diagnosis model of a hybrid deep neural network with principal component analysis is proposed. Firstly, in order to reduce the complexity of deep learning computation, data pre-processing is performed by principal component analysis (PCA) with feature dimensionality reduction. The preprocessed data is imported into the hybrid deep learning model. The first layer of the model uses a CNN algorithm for denoising and simple feature extraction, the second layer makes use of bi-directional long and short memory (BiLSTM) for greater in-depth extraction of the data with time series features, and the last layer uses an attention mechanism for optimal weight assignment, which can further improve the diagnostic precision. The test accuracy of this model is fully comparable to existing deep learning fault diagnosis models, especially under low load; the test accuracy is 100% at constant load and nearly 90% for variable load, and the test accuracy is 72.8% at extreme variable load (2.205 N·m/s-0.735 N·m/s and 0.735 N·m/s-2.205 N·m/s), which are the worst possible load conditions. The experimental results fully prove that the model has reliable robustness and generality.

Model-Based Explanation of Feedback Effects in Syllogistic Reasoning

For decades, a significant number of models explaining human syllogistic inference processes were developed. There is profound work fitting the models' parameters and analyzing each model's ability to account for the data in order to support or reject the underlying theories. However, the model parameters are rarely used to extract explanations and hypotheses for phenomena that go beyond the original scope of the models. In this work, we apply three state-of-the-art models,  the probability heuristics model (PHM), mReasoner, and TransSet, to data from reasoning experiments where participants received feedback for their conclusions. We derived hypotheses based on the models' explanations for the feedback effect and put these to the test by conducting an experiment targeting the hypotheses. The work contributes to the field in three ways: (a) the feedback effect could be replicated and was shown to be a robust effect; (b) we demonstrate the use of the model parameters in order to derive new hypotheses; (c) we present possible explanations for the feedback effect based on existing theories.

Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Surveys on explainable artificial intelligence (XAI) are related to biology, clinical trials, fintech management, medicine, neurorobotics, and psychology, among others. Prognostics and health management (PHM) is the discipline that links the studies of failure mechanisms to system lifecycle management. There is a need, which is still absent, to produce an analytical compilation of PHM-XAI works. In this paper, we use preferred reporting items for systematic reviews and meta-analyses (PRISMA) to present a state of the art on XAI applied to PHM of industrial assets. This work provides an overview of the trend of XAI in PHM and answers the question of accuracy versus explainability, considering the extent of human involvement, explanation assessment, and uncertainty quantification in this topic. Research articles associated with the subject, since 2015 to 2021, were selected from five databases following the PRISMA methodology, several of them related to sensors. The data were extracted from selected articles and examined obtaining diverse findings that were synthesized as follows. First, while the discipline is still young, the analysis indicates a growing acceptance of XAI in PHM. Second, XAI offers dual advantages, where it is assimilated as a tool to execute PHM tasks and explain diagnostic and anomaly detection activities, implying a real need for XAI in PHM. Third, the review shows that PHM-XAI papers provide interesting results, suggesting that the PHM performance is unaffected by the XAI. Fourth, human role, evaluation metrics, and uncertainty management are areas requiring further attention by the PHM community. Adequate assessment metrics to cater to PHM needs are requested. Finally, most case studies featured in the considered articles are based on real industrial data, and some of them are related to sensors, showing that the available PHM-XAI blends solve real-world challenges, increasing the confidence in the artificial intelligence models' adoption in the industry.

Determination of morphologic and immunohistochemical stain (p57 kip2) discrepancy of complete and partial hydatidiform mole by using microsatellite genotyping

OBJECTIVE

to evaluate the role of microsatellite genotyping in discordant results between morphologic examination and p57^Kip2 staining in hydatidiform mole.

MATERIALS AND METHODS

127 cases of hydatidiform mole who had morphologic examination and p57^Kip2immunohistochemical staining were evaluated. Six discrepant cases between morphologic examination and p57^Kip2 staining were recruited. DNA was extracted from chorionic villi and paired maternal decidual tissue in Formalin fixed paraffin embedded tissue sections. The STR DNA genotyping was performed by Applied Biosystems 3500 Genetic Analyzer. Genetic data analysis was performed by Gene mapper ID-X software. Three concordant cases were used as control. Results were compared to histopathology, p57^Kip2 stain and development of post-molar GTN.

RESULTS

All controlled cases were confirmed PHM. Two cases of histologic CHM with positive p57^Kip2and 2 cases of PHM with negative p57^Kip2 were reported as PHM from microsatellite. Other 2 cases of histologic diagnosis PHM with negative p57^Kip2 reported as CHM from microsatellite test and both of them developed post-molar GTN.

CONCLUSION

Microsatellite genotyping is a high accuracy method for differential diagnosis from complete and partial hydatidiform moles. However, cost of microsatellite genotyping is still too high to use routinely. Therefore, selected use in discrepancy cases may be suitable.

A Comprehensive Evaluation Method of Sensor Selection for PHM Based on Grey Clustering

Sensor selection plays an essential and fundamental role in prognostics and health management technology, and it is closely related to fault diagnosis, life prediction, and health assessment. The existing methods of sensor selection do not have an evaluation standard, which leads to different selection results. It is not helpful for the selection and layout of sensors. This paper proposes a comprehensive evaluation method of sensor selection for prognostics and health management (PHM) based on grey clustering. The described approach divides sensors into three grey classes, and defines and quantifies three grey indexes based on a dependency matrix. After a brief introduction to the whitening weight function, we propose a combination weight considering the objective data and subjective tendency to improve the effectiveness of the selection result. Finally, the clustering result of sensors is obtained by analyzing the clustering coefficient, which is calculated based on the grey clustering theory. The proposed approach is illustrated by an electronic control system, in which the effectiveness of different methods of sensor selection is compared. The result shows that the technique can give a convincing analysis result by evaluating the selection results of different methods, and is also very helpful for adjusting sensors to provide a more precise result. This approach can be utilized in sensor selection and evaluation for prognostics and health management.

Getting the Most Out of Your Crystals: Data Collection at the New High-Flux, Microfocus MX Beamlines at NSLS-II

Advances in synchrotron technology are changing the landscape of macromolecular crystallography. The two recently opened beamlines at NSLS-II-AMX and FMX-deliver high-flux microfocus beams that open new possibilities for crystallographic data collection. They are equipped with state-of-the-art experimental stations and automation to allow data collection on previously intractable crystals. Optimized data collection strategies allow users to tailor crystal positioning to optimally distribute the X-ray dose over its volume. Vector data collection allows the user to define a linear trajectory along a well diffracting volume of the crystal and perform rotational data collection while moving along the vector. This is particularly well suited to long, thin crystals. We describe vector data collection of three proteins-Akt1, PI3Kα, and CDP-Chase-to demonstrate its application and utility. For smaller crystals, we describe two methods for multicrystal data collection in a single loop, either manually selecting multiple centers (using H108A-PHM as an example), or "raster-collect", a more automated approach for a larger number of crystals (using CDP-Chase as an example).

A Weighted Deep Representation Learning Model for Imbalanced Fault Diagnosis in Cyber-Physical Systems

Predictive maintenance plays an important role in modern Cyber-Physical Systems (CPSs) and data-driven methods have been a worthwhile direction for Prognostics Health Management (PHM). However, two main challenges have significant influences on the traditional fault diagnostic models: one is that extracting hand-crafted features from multi-dimensional sensors with internal dependencies depends too much on expertise knowledge; the other is that imbalance pervasively exists among faulty and normal samples. As deep learning models have proved to be good methods for automatic feature extraction, the objective of this paper is to study an optimized deep learning model for imbalanced fault diagnosis for CPSs. Thus, this paper proposes a weighted Long Recurrent Convolutional LSTM model with sampling policy (wLRCL-D) to deal with these challenges. The model consists of 2-layer CNNs, 2-layer inner LSTMs and 2-Layer outer LSTMs, with under-sampling policy and weighted cost-sensitive loss function. Experiments are conducted on PHM 2015 challenge datasets, and the results show that wLRCL-D outperforms other baseline methods.

Mass spectrometric evidence for neuropeptide-amidating enzymes in

Neuropeptides constitute a vast and functionally diverse family of neurochemical signaling molecules and are widely involved in the regulation of various physiological processes. The nematode Caenorhabditis elegans is well-suited for the study of neuropeptide biochemistry and function, as neuropeptide biosynthesis enzymes are not essential for C. elegans viability. This permits the study of neuropeptide biosynthesis in mutants lacking certain neuropeptide-processing enzymes. Mass spectrometry has been used to study the effects of proprotein convertase and carboxypeptidase mutations on proteolytic processing of neuropeptide precursors and on the peptidome in C. elegans However, the enzymes required for the last step in the production of many bioactive peptides, the carboxyl-terminal amidation reaction, have not been characterized in this manner. Here, we describe three genes that encode homologs of neuropeptide amidation enzymes in C. elegans and used tandem LC-MS to compare neuropeptides in WT animals with those in newly generated mutants for these putative amidation enzymes. We report that mutants lacking both a functional peptidylglycine α-hydroxylating monooxygenase and a peptidylglycine α-amidating monooxygenase had a severely altered neuropeptide profile and also a decreased number of offspring. Interestingly, single mutants of the amidation enzymes still expressed some fully processed amidated neuropeptides, indicating the existence of a redundant amidation mechanism in C. elegans All MS data are available via ProteomeXchange with the identifier PXD008942. In summary, the key steps in neuropeptide processing in C. elegans seem to be executed by redundant enzymes, and loss of these enzymes severely affects brood size, supporting the need of amidated peptides for C. elegans reproduction.

Quick health assessment for industrial robot health degradation and the supporting advanced sensing development

Robotic technologies are becoming more integrated with complex manufacturing environments. The addition of greater complexity leads to more sources of faults and failures that impact a robot system's reliability. Industrial robot health degradation needs to be assessed and monitored to minimize unexpected shutdowns, improve maintenance techniques, and optimize control strategies. A quick health assessment methodology is developed at the U.S. National Institute of Standards and Technology (NIST) to quickly assess a robot's tool center position and orientation accuracy degradation. An advanced sensing development approach to support the quick health assessment methodology is also presented in this paper. The advanced sensing development approach includes a seven-dimensional (7-D) measurement instrument (time, X, Y, Z, roll, pitch, and yaw) and a smart target to facilitate the quick measurement of a robot's tool center accuracy.

Reaction mechanism of the bicopper enzyme peptidylglycine α-hydroxylating monooxygenase

Peptidylglycine α-hydroxylating monooxygenase is a noninteracting bicopper enzyme that stereospecifically hydroxylates the terminal glycine of small peptides for its later amidation. Neuroendocrine messengers, such as oxytocin, rely on the biological activity of this enzyme. Each catalytic turnover requires one oxygen molecule, two protons from the solvent, and two electrons. Despite this enzyme having been widely studied, a consensus on the reaction mechanism has not yet been found. Experiments and theoretical studies favor a pro-S abstraction of a hydrogen atom followed by the rebinding of an OH group. However, several hydrogen-abstracting species have been postulated; because two protons are consumed during the reaction, several protonation states are available. An electron transfer between the copper atoms could play a crucial role for the catalysis as well. This leads to six possible abstracting species. In this study, we compare them on equal footing. We perform quantum mechanics/molecular mechanics calculations, considering the glycine hydrogen abstraction. Our results suggest that the most likely mechanism is a protonation of the abstracting species before the hydrogen abstraction and another protonation as well as a reduction before OH rebinding.