Early Alerts Research Articles

A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients

Background: Over the past three years, zinc deficiency among adolescents has varied based on region and access to healthcare. Globally, zinc deficiency affects approximately 2 billion people, leading to serious issues such as immune problems and growth delays, particularly in developing countries. In the U.S., around 10% of adolescents experienced zinc deficiency in 2021, with a higher prevalence among teenage girls. In Europe, deficiency rates are generally low but can be significant in Eastern Europe and Central Asia. In Asia, particularly in rural and low-income areas, deficiency rates range from 20 - 30%. In Turkey, the prevalence is high due to poor nutrition. Objectives: This study aimed to develop a machine learning-based decision support system to determine zinc deficiency in children and adolescents aged 10 - 18 years. Methods: This machine learning-based study was conducted with 370 adolescents aged 10 - 18 years to assess their zinc deficiency. The dataset consists of 8 feature vectors and an output vector. The machine learning methods used in the analysis include Logistic Regression, Naive Bayes, Decision Tree (CART), K-NN (K-Nearest Neighbors), SVM (Support Vector Machine), Gradient Boosting Classifier, AdaBoost Classifier, Bagging Classifier, Random Forest Classifier, MLP Classifier (Multilayer Perceptron), and XGB Classifier (XGBoost Classifier). Evaluation metrics such as accuracy, precision, recall, and F1 score were used to assess the performance of these methods. Including specific values for these metrics, such as "SVM achieved 94.6% accuracy," would allow readers to quickly compare the effectiveness of the models. Different metrics serve various purposes: Accuracy provides an overall view of performance, precision and recall highlight specific aspects, and the F1 score balances precision and recall. Results: The mean age of the patients in the dataset was 13.79 ± 1.18 years. Of the children, 64.32% (n = 238) were female and 35.68% (n = 132) were male. It was found that 62.7% (n = 232) of the children had low zinc levels, while 37.3% (n = 138) did not require zinc supplementation. Thirteen different machine learning methods were applied to a 70% training and 30% testing set. As a result, the SVM method provided the most successful outcome with 94.6% accuracy. Implementing the SVM-based system in pediatric clinics could improve efficiency and patient care by automatically detecting high-risk zinc deficiency patients based on lab results, providing early intervention alerts for faster treatment, and improving health outcomes. Highlighting these practical applications could increase the study’s appeal to healthcare professionals by demonstrating its real-world benefits. Providing detailed information on these applications would enhance the study’s clarity and practical value, making it more valuable for researchers and healthcare providers interested in AI tools for adolescent health. Conclusions: This study concluded that machine learning methods can effectively determine zinc deficiency in children. The SVM method demonstrated superior classification performance compared to the other methods. An SVM-based decision support system could be integrated into pediatric outpatient clinics to enhance diagnostic accuracy and patient care.

Prediction of approaching trains based on H‐ranks of track vibration signals

AbstractThis paper introduces a method for forecasting the arrival of trains by analyzing track vibration signals. The proposed algorithms, based on H‐ranks of track vibration signals, can generate early alerts for approaching trains. These algorithms are robust to additive noise and environmental conditions. The theoretical foundation of the method involves the application of matrix operations to detect significant changes in vibration patterns, indicating an approaching train.

MOS-based Gas Sensors for Early Alert of Thermal Runaway in Lithium-ion Batteries

MOS-based Gas Sensors for Early Alert of Thermal Runaway in Lithium-ion Batteries

Napping during cognitive behavioural therapy for insomnia: Friends or foes?

SummaryCognitive and Behavioural Therapy for Insomnia (CBT‐I) is the gold standard treatment for chronic insomnia, with one crucial step being the restriction of time spent in bed. This restriction often intensifies early afternoon sleepiness, leading to a natural gateway for a short recuperative nap, which might foster adherence to CBT‐I over time. In practice, mental health professionals providing CBT‐I lack consensus on whether or not to tolerate short naps during the CBT‐I period for requesting patients. In this pilot study, we examined the effects of authorised napping on CBT‐I efficiency in patients with insomnia (a napping group was compared with a matched non‐napping group, n = 108). We report that napping enhanced early afternoon alertness and importantly did not affect CBT‐I‐mediated improvements in the Insomnia Severity Index and Beck Depression Inventory‐2 and in self‐reported sleep efficiency, latency, and wake after sleep onset (assessed by the sleep diaries). Further investigations using objective methods of sleep assessments are now needed to confirm that napping behaviour does not compromise the improvements enabled by CBT‐I and may even strengthen adherence to the treatment.

IoT based Smart Weather Monitoring with Flood and Earthquake Detection

Weather monitoring is essential for understanding the changing climatic conditions. Traditional weather monitoring system are slow, labor intensive, and outdated, often prone to error and inaccurate predictions. To overcome these challenges and provide an up-to date weather information, the IoT (Internet of Things) has been implemented in weather monitoring. This research presents an IoT-based weather monitoring system integrated with sensors to ensure accurate weather monitoring, including flood and earth quake detection. The collected data, such as temperature, humidity, atmospheric pressure, rainfall, flood levels, and seismic activity, is transmitted through IoT protocols to a centralized repository (Cloud) and visualized in the Blynk App. Flood sensors are strategically positioned in flood-prone areas, continuously monitoring water levels to detect abnormal rises indicative of potential floods. Similarly, seismic sensors detect ground vibrations and seismic activity, providing early alerts for earthquakes. The proposed system offers a comprehensive approach to monitor the weather, contributing to enhanced preparedness and mitigation strategies for natural disasters.

Seismic isolation systems for next-generation gravitational wave detectors

Gravitational Wave (GW) Astronomy is becoming a revolutionary method for studying the Universe and understanding its evolution, opening a new frontier in the exploration of cosmic phenomena. Third-generation ground-based gravitational wave interferometers, expected to be built around 2035, will be an order of magnitude more sensitive than the current detectors. Their low frequency sensitivity will allow the detection of binary compact coalescences up to high red-shift improving the capability to study intermediate-mass black holes and enhancing early alerts of binary neutron star coalescences. Within this scientific contest an important effort is being made to increase the detection capabilities in a frequency range down to 3 Hz and mitigating low-frequency noise. The Superattenuator is the mechanical device designed to isolate the optical components of the Advanced VIRGO (AdV) interferometer from seismic noise and local disturbances, relying on the passive and active action of the elements. In this short note, we summarize the design activity around new generation of seismic attenuation systems with the intent to improve the sensitivity in the low frequencies region. The optimization of prototypes and their passive attenuation performance is also considered in view of the Einstein Telescope with the possibility to decrease the size of the mechanical structure and minimizing the impact on the civil works for the construction of the underground laboratory.

DIFFERENTIATED VULVAR INTRAEPITHELIAL NEOPLASIA: A CLINICAL EXAMPLE OF INTERDISCIPLINARY PATIENT MANAGEMENT

Differentiated vulvar intraepithelial neoplasia (dVIN) is an obligate precancer, develops during chronic vulvodermatoses, such as lichen sclerosus and lichen simplex chronicus. The epidemiological, clinical, pathomorphological, diagnostic, and prognostic features of dVIN are discusses. A clinical case of interdisciplinary management of a patient with dVIN, complicated by malignancy and urethral stricture, is presented. Early oncological alertness was demonstrated, microinvasive vulvar cancer in woman with of lichen sclerosus was diagnosed. It is actually, because there is high amount primary patients with advanced vulvar cancer. The joint work of oncologists, urologists, and gynecologists made it possible to successfully perform reconstructive plastic surgery for urethral stricture using a buccal graft for the first time in the Trans-Baikal Territory. Thanks to this the urinary function was restored and the quality of life improved.

FPGA implementation of automatic seizure detection in EEG signals using machine learning algorithm

This work presents a novel approach that harnesses the capabilities of field programmable gate arrays (FPGA) to enable instantaneous monitoring of electroencephalography (EEG) signals for the detection and prediction of seizure patterns. The integration of FPGA technology with the perceptron learning algorithm holds promises for enhancing real-time healthcare monitoring systems and contributing to improved patient care and safety. The proposed work addresses the challenges presented at predicting seizures from EEG data. The first challenge is to handle massive EEG data that poses memory issues. To tackle this, the research employs cellular automata (Rule 90 and Rule 150) to reduce data size by 85.71%, making it more manageable. The second is to implement a linearly classified perceptron algorithm using FPGA to predict and detect seizures from real-time EEG data. The third is clock synchronization between stored data and real EEG data for comparison purposes. The proposed system is continuously compared to real-time EEG signals using a single perceptron neural network, and alert signals are generated based on preactivation values, with a continuous alert issued when the value reaches 3/4th sample match. These early alerts empower individuals to take preventive actions. The model is implemented using an FPGA Zynq-7000 series, which consumes 270mW of power. The design utilizes 118 Lookup Tables (LUTs).

A Deep Learning-Based Emergency Alert Wake-Up Signal Detection Method for the UHD Broadcasting System.

With the increasing frequency and severity of disasters and accidents, there is a growing need for efficient emergency alert systems. The ultra-high definition (UHD) broadcasting service based on Advanced Television Systems Committee (ATSC) 3.0, a leading terrestrial digital broadcasting system, offers such capabilities, including a wake-up function for minimizing damage through early alerts. In case of a disaster situation, the emergency alert wake-up signal is transmitted, allowing UHD TVs to be activated, enabling individuals to receive emergency alerts and access emergency broadcasting content. However, conventional methods for detecting the bootstrap signal, essential for this function, typically require an ATSC 3.0 demodulator. In this paper, we propose a novel deep learning-based method capable of detecting an emergency wake-up signal without the need for an ATSC 3.0. The proposed method leverages deep learning techniques, specifically a deep neural network (DNN) structure for bootstrap detection and a convolutional neural network (CNN) structure for wake-up signal demodulation and to detect the bootstrap and 2 bit emergency alert wake-up signal. Specifically, our method eliminates the need for Fast Fourier Transform (FFT), frequency synchronization, and interleaving processes typically required by a demodulator. By applying a deep learning in the time domain, we simplify the detection process, allowing for the detection of an emergency alert signal without the full suite of demodulator components required for ATSC 3.0. Furthermore, we have verified the performance of the deep learning-based method using ATSC 3.0-based RF signals and a commercial Software-Defined Radio (SDR) platform in a real environment.

Early Detection of 5 Neurodevelopmental Disorders of Children and Prevention of Postnatal Depression With a Mobile Health App: Observational Cross-Sectional Study.

Delay in the diagnosis of neurodevelopmental disorders (NDDs) in toddlers and postnatal depression (PND) is a major public health issue. In both cases, early intervention is crucial but too rarely implemented in practice. Our goal was to determine if a dedicated mobile app can improve screening of 5 NDDs (autism spectrum disorder [ASD], language delay, dyspraxia, dyslexia, and attention-deficit/hyperactivity disorder [ADHD]) and reduce PND incidence. We performed an observational, cross-sectional, data-based study in a population of young parents in France with at least 1 child aged <10 years at the time of inclusion and regularly using Malo, an "all-in-one" multidomain digital health record electronic patient-reported outcome (PRO) app for smartphones. We included the first 50,000 users matching the criteria and agreeing to participate between May 1, 2022, and February 8, 2024. Parents received periodic questionnaires assessing skills in neurodevelopment domains via the app. Mothers accessed a support program to prevent PND and were requested to answer regular PND questionnaires. When any PROs matched predefined criteria, an in-app recommendation was sent to book an appointment with a family physician or pediatrician. The main outcomes were the median age of the infant at the time of notification for possible NDD and the incidence of PND detection after childbirth. One secondary outcome was the relevance of the NDD notification by consultation as assessed by health professionals. Among 55,618 children median age 4 months (IQR 9), 439 (0.8%) had at least 1 disorder for which consultation was critically necessary. The median ages of notification for probable ASD, language delay, dyspraxia, dyslexia, and ADHD were 32.5 (IQR 12.8), 16 (IQR 13), 36 (IQR 22.5), 80 (IQR 5), and 61 (IQR 15.5) months, respectively. The rate of probable ADHD, ASD, dyslexia, language delay, and dyspraxia in the population of children of the age included between the detection limits of each alert was 1.48%, 0.21%, 1.52%, 0.91%, and 0.37%, respectively. Sensitivity of alert notifications for suspected NDDs as assessed by the physicians was 78.6% and specificity was 98.2%. Among 8243 mothers who completed a PND questionnaire, highly probable PND was detected in 938 (11.4%), corresponding to a reduction of -31% versus our previous study without a support program. Suspected PND was detected a median 96 days (IQR 86) after childbirth. Among 130 users who filled in the satisfaction survey, 99.2% (129/130) found the app easy to use and 70% (91/130) reported that the app improved follow-up of their child. The app was rated 4.8/5 on Apple's App Store. Algorithm-based early alerts suggesting NDDs were highly specific with good sensitivity as assessed by real-life practitioners. Early detection of 5 NDDs and PNDs was efficient and led to a possible 31% reduction in PND incidence. ClinicalTrials.gov NCT06301087; https://www.clinicaltrials.gov/study/NCT06301087.

The Ojai California Earthquake of 20 August 2023: Earthquake Early Warning Performance and Alert Recipient Response in the Mw 5.1 Event

Abstract A magnitude 5.1 earthquake in California rarely generates more than momentary notice—a headline in local newspapers and a mention with footage on the evening news—then fades into obscurity for most people. But this earthquake, which occurred near the city of Ojai, is important for seismologists, social scientists, emergency managers, policymakers, and others who are engaged in implementing and improving earthquake early warning (EEW) technology and in assessing its value in public warnings. In this earthquake, ShakeAlert, the EEW system for the West Coast of the United States operated by the U.S. Geological Survey (USGS), was publicly activated and, for the first time, a substantial number of those who received alerts provided feedback on various aspects of the alerts they received. To capture data related to public attitudes and assessments regarding this and future alerts, a supplemental questionnaire was developed and associated with the “Did You Feel It?” (DYFI) earthquake reporting system, also operated by the USGS. The DYFI system received over 14,000 felt reports; 2490 of these were by people who received or expected to receive an alert before the onset of earthquake motion at their locations. This article analyzes the aggregate results of these EEW user reports, touching on the respondent’s situation upon receiving the alert, characteristics of the alert received, and, perhaps, most importantly, how the alert recipient responded if received before feeling earthquake motion. The new DYFI EEW supplemental questionnaire also inquired about respondent views of alert usefulness and preferences in future alerts. Our report provides a first glimpse of a range of behaviors, attitudes, and assessments by users of the recently implemented EEW system for the U.S. West Coast.

Evaluating the Short-term Causal Effect of Early Alert on Student Performance

Evaluating the Short-term Causal Effect of Early Alert on Student Performance

Revealing crack initiation and extension of brittle rock via time-lapse acoustic attenuation: Insights from active ultrasonic experiments

Predicting brittle rock failure is essential for rock mechanics research and early disaster alerts in deep rock engineering. Successful prediction relies on identifying crack initiation and extension. However, currently, there is no universally accepted method for accurately identifying and tracking crack evolution to predict brittle failure of the surrounding rock. In this study, the propagation parameters of transmission ultrasonic waves were used to characterize crack behaviors at the laboratory scale. The differential acoustic attenuation term was proposed by modifying the spectral ratio method to evaluate the dissipation capability of the corresponding medium to the transmitted ultrasonic waves. In addition to acoustic attenuation, velocity and amplitude attenuation were introduced to characterize the damage evolution during brittle failure. The indicators were analyzed by conducting a uniaxial compression test on a sandstone sample with a single flaw. During compression, active ultrasonic surveys were conducted to attain the propagation parameters. The two-dimensional digital image correlation method was simultaneously applied to capture the full-field strain on the front surface of the sample. The shear and tensile strain profiles indicated that the shear damage initially accumulated at the tips of the flaw, causing a reduction in the velocity and amplitude of the transmitted ultrasonic waves. The acoustic attenuation indicator in the flaw-tip regions increased before the shear strain began. As the damage developed further, the acoustic attenuation increased overall, with numerous local fluctuations in the flaw-tip regions due to stress rearrangement and damage development, while the velocity and amplitude remained consistently low. In contrast, intact regions that were far away from the tips of the flaw exhibited different characteristics. The velocity and amplitude variations were more staged, while the acoustic attenuation changed gradually. The results indicated that the variations in the propagation parameters exhibited specific patterns before the damage was initiated, offering a potential for predicting crack initiation. The variations in the acoustic attenuation, velocity, and amplitude attenuation allow for tracking the crack propagation.

IoT Based Early Flood Alerting System

The frequency and intensity of floods have increased recently in many parts of the world, increasing the need for cutting-edge technology solutions to mitigate the effects of these natural disasters. It is crucial to keep aneye on the water flow and get early emergency alerts regarding the water level based on the riverbed in order toprevent such disasters. The goal of this project is to create asystem that employs cutting-edge sensors and a Wi-Fi module to detect the water level. The suggested system has a number of sensors that can track important variables, including temperature, humidity, and water level. If the level crosses a certain threshold, The system will send out early warnings to everyone, alerting them to the likelihood of floods. To process and store data, we have linked the Arduino UNO to each sensor. The system can notify a wider audience by sending email alerts, ensuring that individuals in flood-prone areas receive timely warnings. Additionally, the use ofa Wi-Fi module enables real-time data transmission and remote monitoring of water levels, allowing authorities to take preemptive measures and minimize the impact of potential floods. By integrating advanced sensors with communication technology, this project aims to enhance early warning systems and contribute to more effective disaster management strategies in vulnerable regions. Ultimately, the implementation of such innovative solutions can significantly improve community resilience and reduce the adverse consequences of flooding events.

Gas Outburst Warning Method in Driving Faces: Enhanced Methodology through Optuna Optimization, Adaptive Normalization, and Transformer Framework

Addressing common challenges such as limited indicators, poor adaptability, and imprecise modeling in gas pre-warning systems for driving faces, this study proposes a hybrid predictive and pre-warning model grounded in time-series analysis. The aim is to tackle the effects of broad application across diverse mines and insufficient data on warning accuracy. Firstly, we introduce an adaptive normalization (AN) model for standardizing gas sequence data, prioritizing recent information to better capture the time-series characteristics of gas readings. Coupled with the Gated Recurrent Unit (GRU) model, AN demonstrates superior forecasting performance compared to other standardization techniques. Next, Ensemble Empirical Mode Decomposition (EEMD) is used for feature extraction, guiding the selection of the Variational Mode Decomposition (VMD) order. Minimal decomposition errors validate the efficacy of this approach. Furthermore, enhancements to the transformer framework are made to manage non-linearities, overcome gradient vanishing, and effectively analyze long time-series sequences. To boost versatility across different mining scenarios, the Optuna framework facilitates multiparameter optimization, with xgbRegressor employed for accurate error assessment. Predictive outputs are benchmarked against Recurrent Neural Networks (RNN), GRU, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), where the hybrid model achieves an R-squared value of 0.980975 and a Mean Absolute Error (MAE) of 0.000149, highlighting its top performance. To cope with data scarcity, bootstrapping is applied to estimate the confidence intervals of the hybrid model. Dimensional analysis aids in creating real-time, relative gas emission metrics, while persistent anomaly detection monitors sudden time-series spikes, enabling unsupervised early alerts for gas bursts. This model demonstrates strong predictive prowess and effective pre-warning capabilities, offering technological reinforcement for advancing intelligent coal mine operations.

DROWSINESS PREVENTION SYSTEM FOR DRIVERS

The Anti-Fatigue Alert System is a vital invention meant to tackle the dangerous problem of driver sleepiness, which is a major cause of traffic accidents globally. This device uses a variety of behavioural and physiological markers to identify when a driver starts to become sleepy, giving early alerts to help avoid potential collisions. The alert system keeps track of the alertness levels of drivers using sophisticated sensor technologies and algorithms. When fatigue signals are recognised, it intervenes with an aural, visual, or vibrating signal. The creation, operation, and efficacy of anti-fatigue alert system in reducing the dangers of driver drowsiness are examined in this abstract, which highlights the essential role these devices play in improving traffic safety and preventing fatalities. Keywords: Fatigue, Alert system, Safety management.

Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers

Electric motors play a fundamental role in various industries, and their relevance is strengthened in the context of the energy transition. Having efficient tools and techniques to detect and diagnose faults in electrical machines is crucial, as is providing early alerts to facilitate prompt decision-making. This study proposes indicators based on the magnitude of the space vector stator current for detecting and diagnosing incipient inter-turn short circuits (ITSCs) in induction motors (IMs). The effectiveness of these indicators was evaluated using four machine learning methods previously documented in the literature: random forests (RFs), support vector machines (SVMs), the k-nearest neighbor (kNN), and feedforward and recurrent neural networks (FNNs and RNNs). This assessment was conducted using experimental data. The results were compared with indicators based on discrete wavelet transform (DWT), demonstrating the viability of the proposed approach, which opens up a way of detecting incipient ITSCs in three-phase IMs. Furthermore, utilizing features derived from the magnitude of the spatial vector led to the successful identification of the phase affected by the fault.

Similarity indicator and CG-CGAN prediction model for remaining useful life of rolling bearings

To tackle the challenges of performing early fault warning and improving the prediction accuracy for the remaining useful life (RUL) of rolling bearings, this paper proposes a similarity health indicator and a predictive model of CG-conditional generative adversarial network (CGAN), which relies on a CGAN that combines one-dimensional convolutional neural network (CNN) with a bidirectional gate recurrent unit (Bi-GRU). This framework provides a comprehensive theoretical foundation for RUL prediction of rolling bearings. The similarity health indicator allows for early fault warning of rolling bearings without expert knowledge. Within the CGAN framework, the inclusion of constraints guides the generation of samples in a more targeted manner. Additionally, the proposed CG-CGAN model incorporates Bi-GRU to consider both forward and backward information, thus improving the precision of RUL forecasting. Firstly, the similarity indicator between the vibration signals of the rolling bearing over its full life span and the standard vibration signals (healthy status) is calculated. This indicator helps to determine the early deterioration points of the rolling bearings. Secondly, the feature matrix composed of traditional health indicators and similarity health indicator, is utilized to train and test the proposed CG-CGAN model for RUL prediction. Finally, to corroborate the efficacy of the proposed method, two sets of real experiment data of rolling bearing accelerated life from the Intelligent Maintenance Systems (IMS) are utilized. Experimental findings substantiate that the proposed similarity health indicator offers early fault alerts and precisely delineates the performance diminution of the rolling bearing. Furthermore, the put-forward CG-CGAN model achieves high-precision RUL prediction of rolling bearing.

When grey model meets deep learning: A new hazard classification model

The classification of hazard is critical in industrial informatics, as it enhances early safety alerts, supports decision-making, and facilitates policy assessment. However, previous studies have generally neglected the temporal attributes of hazards, thereby constraining the effectiveness of models. This paper introduces a new model for hazard classification termed DLGM. DLGM represents a deep learning framework, with the structural parameters of grey models to encapsulate the hazard temporal attributes. To better accommodate the fluctuations of hazard series, a new grey model termed FSGM(1,1) equipped with Fourier series is proposed. Moreover, DLGM leverages a novel hierarchical feature fusion neural network (HFFNN) to optimize feature processing. Extensive experiments across three hazard themes involving 18 large-scale industrial processes have demonstrated the competitiveness of DLGM (e.g., it surpasses benchmark models such as Random Forest and BERT by approximately 2% in both accuracy and F1), the suitability of FSGM(1,1) (e.g., its mean absolute percentage error is less than 10% and mean squared error is below 0.02) and the effectiveness of HFFNN (e.g., it enhances the accuracy and F1 of DLGM by about 1%).

Field Validation of a Universally Applicable Condition-Based Maintenance System for Mud Pumps

Summary Although mud pumps are considered to be critical rig equipment, their health monitoring currently still relies on infrequent human observation and monitoring. This approach often fails to detect pump damage at an early stage, resulting in nonproductive time (NPT) and increased well construction costs when initial damage progresses and pumps go down unexpectedly and catastrophically. Automated approaches to condition-based maintenance (CBM) of mud pumps to date have failed due to the lack of a generalized solution applicable to any pump type and/or operating conditions. This paper presents a field-validated universally applicable solution to mud pump CBM. The system uses a sensor package that includes acoustic emission sensors and accelerometers in combination with anomaly detection deep learning data analysis to pinpoint any abnormal behavior of the pump and its components. The deep learning models are trained with undamaged normal state data only, and a damage score characterizing the extent of damage to the mud pump is calculated to identify the earliest signs of damage. The system can then generate alerts to notify the rig crew of the damage level of key mud pump components, prompting proactive maintenance actions. Field tests were conducted while drilling an unconventional shale well in west Texas, USA, and a geothermal well in Japan (i.e., two very different drilling operations) to verify the feasibility and general applicability of the developed pump CBM solution. Sensors were attached to pump modules, and data were collected and analyzed using the deep learning models during drilling operations. During the field tests, different hyperparameters and features were compared to select the most effective ones for identifying damage while at the same time delivering low false positive rates (i.e., false alarms during normal state pump operation). The system required only several hours of normal state data for training with no prior pump information. Moreover, it correctly identified the degradation of the pump, swabs, and valves and produced early alerts several hours (in the range of 0.5–17 hours) before actual pump maintenance action was taken by the rig crew. This generally applicable pump CBM system eliminates the environmental, health, and safety concerns that can occur during human-based observations of mud pump health and avoids unnecessary NPT associated with catastrophic pump failures. The final version of this system will be a fully self-contained magnetically attachable box containing sensors and a processor, generating simple indicators for recommending proactive pump maintenance tasks when needed.