Deep Learning-Based Prognostics and Health Management Model for Pilot-Operated Cryogenic Safety Valves.

This paper highlights the significance of safety and reliability in modern industries, particularly in sectors like petroleum and LNG, where safety valves play a critical role in ensuring system safety under extreme conditions. To enhance the reliability of these valves, this study aims to develop a deep learning-based prognostics and health management (PHM) model. Past empirical methods have limitations, driving the need for data-driven prediction models. The proposed model monitors safety valve performance, detects anomalies in real time, and prevents accidents caused by system failures. The research focuses on collecting sensor data, analyzing trends for lifespan prediction and normal operation, and integrating data for anomaly detection. This study compares related research and existing models, presents detailed results, and discusses future research directions. Ultimately, this research contributes to the safe operation and anomaly detection of pilot-operated cryogenic safety valves in industrial settings.

PHM6 and PHM7 genes are essential for phosphate surplus in the cells of Saccharomyces cerevisiae.

The cells of Saccharomyces cerevisiae are capable for phosphate surplus: the increased uptake of phosphate (Pi) and accumulation of inorganic polyphosphate (polyP) occur when the cells after Pi limitation were cultivated in a medium supplemented with Pi. We demonstrated that single knockout mutations in the PHO84, PHO87, and PHO89 genes encoding plasma membrane phosphate transporters suppressed the Pi uptake and polyP accumulation under phosphate surplus at nitrogen starvation. The knockout strains in the PHM6 and PHM7 genes encoding unannotated PHO-proteins showed decreased polyP accumulation under Pi surplus both at nitrogen starvation and in complete YPD medium. This is due to the suppression of Pi uptake in the cells of these mutant strains. We speculate that Pi transporters of plasma membrane, and Phm6 and Phm7 proteins function in concert providing increased Pi uptake at phosphate surplus conditions.

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.

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.

Time-Frequency Multi-Domain 1D Convolutional Neural Network with Channel-Spatial Attention for Noise-Robust Bearing Fault Diagnosis.

This paper proposes a noise-robust and accurate bearing fault diagnosis model based on time-frequency multi-domain 1D convolutional neural networks (CNNs) with attention modules. The proposed model, referred to as the TF-MDA model, is designed for an accurate bearing fault classification model based on vibration sensor signals that can be implemented at industry sites under a high-noise environment. Previous 1D CNN-based bearing diagnosis models are mostly based on either time domain vibration signals or frequency domain spectral signals. In contrast, our model has parallel 1D CNN modules that simultaneously extract features from both the time and frequency domains. These multi-domain features are then fused to capture comprehensive information on bearing fault signals. Additionally, physics-informed preprocessings are incorporated into the frequency-spectral signals to further improve the classification accuracy. Furthermore, a channel and spatial attention module is added to effectively enhance the noise-robustness by focusing more on the fault characteristic features. Experiments were conducted using public bearing datasets, and the results indicated that the proposed model outperformed similar diagnosis models on a range of noise levels ranging from -6 to 6 dB signal-to-noise ratio (SNR).

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.

From Corrective to Predictive Maintenance-A Review of Maintenance Approaches for the Power Industry.

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

σ-Aromatic MAl6S6 (M = Ni, Pd, Pt) Stars Containing Planar Hexacoordinate Transition Metals.

Hypercoordinate transition-metal species are mainly dominated by the 18-valence-electron (18ve) counting. Herein, we report ternary MAl6S6 (M = Ni, Pd, Pt) clusters with the planar hexacoordinate metal (phM) centers, which feature 16ve counting instead of the classic 18ve rule. These global-minimum clusters are established via unbiased global searches, followed by PBE0 and single-point CCSD(T) calculations. The phM MAl6 units are stabilized by six peripheral bridging S atoms in these star-like species. Chemical bonding analyses reveal that there are 10 delocalized electrons around the phM center, which can render the aromaticity according to the (4n + 2) Hückel rule. It is worth noting that adding an (or two) electron(s) to its π-type lowest unoccupied molecular orbital (LUMO) will make the system unstable.

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.

Deep Scattering Spectrum Germaneness for Fault Detection and Diagnosis for Component-Level Prognostics and Health Management (PHM).

Most methodologies for fault detection and diagnosis in prognostics and health management (PHM) systems use machine learning (ML) or deep learning (DL), in which either some features are extracted beforehand (in the case of typical ML approaches) or the filters are used to extract features autonomously (in the case of DL) to perform the critical classification task. In particular, in the fault detection and diagnosis of industrial robots where the primary sources of information are electric current, vibration, or acoustic emissions signals that are rich in information in both the temporal and frequency domains, techniques capable of extracting meaningful information from non-stationary frequency-domain signals with the ability to map the signals into their constituent components with compressed information are required. This has the potential to minimise the complexity and size of traditional ML- and DL-based frameworks. The deep scattering spectrum (DSS) is one of the approaches that use the Wavelet Transform (WT) analogy for separating and extracting information embedded in a signal's various temporal and frequency domains. Therefore, the primary focus of this work is the investigation of the efficacy and applicability of the DSS's feature domain relative to fault detection and diagnosis for the mechanical components of industrial robots. For this, multiple industrial robots with distinct mechanical faults were studied. Data were collected from these robots under different fault conditions and an approach was developed for classifying the faults using DSS's low-variance features extracted from input signals. The presented approach was implemented on the practical test benches and demonstrated satisfactory performance in fault detection and diagnosis for simple and complex classification problems with a classification accuracy of 99.7% and 88.1%, respectively. The results suggest that, similarly to other ML techniques, the DSS offers significant potential in addressing fault classification challenges, especially for cases where the data are in the form of signals.

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.

Real-Time Prognostics of Engineered Systems under Time Varying External Conditions Based on the COX PHM and VARX Hybrid Approach.

In spite of the development of the Prognostics and Health Management (PHM) during past decades, the reliability prognostics of engineered systems under time-varying external conditions still remains a challenge in such a field. When considering the challenge mentioned above, a hybrid method for predicting the reliability index and the Remaining Useful Life (RUL) of engineered systems under time-varying external conditions is proposed in this paper. The proposed method is competent in reflecting the influence of time-varying external conditions on the degradation behaviour of engineered systems. Based on a subset of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset as case studies, the Cox Proportional Hazards Model (Cox PHM) with time-varying covariates is utilised to generate the reliability indices of individual turbofan units. Afterwards, a Vector Autoregressive model with Exogenous variables (VARX) combined with pairwise Conditional Granger Causality (CGC) tests for sensor selections is defined to model the time-varying influence of sensor signals on the reliability indices of different units that have been previously generated by the Cox PHM with time-varying covariates. During the reliability prediction, the Fourier Grey Model (FGM) is employed with the time series models for long-term forecasting of the external conditions. The results show that the method that is proposed in this paper is competent for the RUL prediction as compared with baseline approaches.

Assessment of a Novel Position Verification Sensor to Identify and Isolate Robot Workcell Health Degradation.

Manufacturing processes have become increasingly sophisticated leading to greater usage of robotics. Sustaining successful manufacturing robotic operations requires a strategic maintenance program. Without careful planning, maintenance can be very costly. To reduce maintenance costs, manufacturers are exploring how they can assess the health of their robot workcell operations to enhance their maintenance strategies. Effective health assessment relies upon capturing appropriate data and generating intelligence from the workcell. Multiple data streams relevant to a robot workcell may be available including robot controller data, a supervisory programmable logic controller data, maintenance logs, process and part quality data, and equipment and process fault and failure data. These data streams can be extremely informative, yet the massive volume and complexity of this data can be overwhelming, confusing, and sometimes paralyzing. Researchers at the National Institute of Standards and Technology have developed a test method and companion sensor to assess the health of robot workcells which will yield an additional and unique data stream. The intent is that this data stream can either serve as a surrogate for larger data volumes to reduce the data collection and analysis burden on the manufacturer, or add more intelligence to assessing robot workcell health. This article presents the most recent effort focused on verifying the companion sensor. Results of the verification test process are discussed along with preliminary results of the sensor's performance during verification testing. Lessons learned indicate that the test process can be an effective means of quantifying the sensor's measurement capability particularly after test process anomalies are addressed in future efforts.

Health Monitoring of Air Compressors Using Reconstruction-Based Deep Learning for Anomaly Detection with Increased Transparency.

Anomaly detection refers to detecting data points, events, or behaviour that do not comply with expected or normal behaviour. For example, a typical problem related to anomaly detection on an industrial level is having little labelled data and a few run-to-failure examples, making it challenging to develop reliable and accurate prognostics and health management systems for fault detection and identification. Certain machine learning approaches for anomaly detection require normal data to train, which reduces the need for historical data with fault labels, where the main task is to differentiate between normal and anomalous behaviour. Several reconstruction-based deep learning approaches are explored in this work and compared towards detecting anomalies in air compressors. Anomalies in such systems are not point-anomalies, but instead, an increasing deviation from the normal condition as the system components start to degrade. In this paper, a descriptive range of the deviation based on the reconstruction-based techniques is proposed. Most anomaly detection approaches are considered black box models, predicting whether an event should be considered an anomaly or not. This paper proposes a method for increasing the transparency and explainability of reconstruction-based anomaly detection to indicate which parts of a system contribute to the deviation from expected behaviour. The results show that the proposed methods detect abnormal behaviour in air compressors accurately and reliably and indicate why it deviates. The proposed approach is capable of detecting faults without the need for historical examples of similar faults. The proposed method for explainable anomaly detection is crucial to any prognostics and health management (PHM) system due to its purpose of detecting deviations and identifying causes.

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.

Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM).

In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%.

Mass spectrometric evidence for neuropeptide-amidating enzymes in Caenorhabditis elegans.

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.

Comparative analysis of myometrial and vascular smooth muscle cells to determine optimal cells for use in drug discovery.

Novel therapeutic regulators of uterine contractility are needed to manage preterm labor, induce labor and control postpartum hemorrhage. Therefore, we previously developed a high-throughput assay for large-scale screening of small molecular compounds to regulate calcium-mobilization in primary mouse uterine myometrial cells. The goal of this study was to select the optimal myometrial cells for our high-throughput drug discovery assay, as well as determine the similarity or differences of myometrial cells to vascular smooth muscle cells (VSMCs)-the most common off-target of current myometrial therapeutics. Molecular and pharmacological assays were used to compare myometrial cells from four sources: primary cells isolated from term pregnant human and murine myometrium, immortalized pregnant human myometrial (PHM-1) cells and immortalized non-pregnant human myometrial (hTERT-HM) cells. In addition, myometrial cells were compared to vascular SMCs. We found that the transcriptome profiles of hTERT-HM and PHM1 cells were most similar (r = 0.93 and 0.90, respectively) to human primary myometrial cells. Comparative transcriptome profiling of primary human myometrial transcriptome and VSMCs revealed 498 upregulated (p ≤ 0.01, log2FC≥1) genes, of which 142 can serve as uterine-selective druggable targets. In the high-throughput Ca2+-assay, PHM1 cells had the most similar response to primary human myometrial cells in OT-induced Ca2+-release (Emax = 195% and 143%, EC50 = 30 nM and 120 nM, respectively), while all sources of myometrial cells showed excellent and similar robustness and reproducibility (Z' = 0.52 to 0.77). After testing a panel of 61 compounds, we found that the stimulatory and inhibitory responses of hTERT-HM cells were highly-correlated (r = 0.94 and 0.95, respectively) to human primary cells. Moreover, ten compounds were identified that displayed uterine-selectivity (≥5-fold Emax or EC50 compared to VSMCs). Collectively, this study found that hTERT-HM cells exhibited the most similarity to primary human myometrial cells and, therefore, is an optimal substitute for large-scale screening to identify novel therapeutic regulators of myometrial contractility. Moreover, VSMCs can serve as an important counter-screening tool to assess uterine-selectivity of targets and drugs given the similarity observed in the transcriptome and response to compounds.