Foundational Models for Fault Diagnosis of Electrical Motors

A majority of recent advancements related to the fault diagnosis of electrical motors are based on the assumption that training and testing data are drawn from the same distribution. However, the data distribution can vary across different operating conditions during real-world operating scenarios of electrical motors. Consequently, this assumption limits the practical implementation of existing studies for fault diagnosis, as they rely on fully labelled training data spanning all operating conditions and assume a consistent distribution. This is because obtaining a large number of labelled samples for several machines across different fault cases and operating scenarios may be unfeasible. In order to overcome the aforementioned limitations, this work proposes a framework to develop a foundational model for fault diagnosis of electrical motors. It involves building a neural network-based backbone to learn high-level features using self-supervised learning, and then fine-tuning the backbone to achieve specific objectives. The primary advantage of such an approach is that the backbone can be fine-tuned to achieve a wide variety of target tasks using very less amount of training data as compared to traditional supervised learning methodologies. The empirical evaluation demonstrates the effectiveness of the proposed approach by obtaining more than 90\% classification accuracy by fine-tuning the backbone not only across different types of fault scenarios or operating conditions, but also across different machines. This illustrates the promising potential of the proposed approach for cross-machine fault diagnosis tasks in real-world applications.Comment: 7 pages, 1 figure, 5 tables, submitted to IEEE PESGRE 202

Active Foundational Models for Fault Diagnosis of Electrical Motors

Fault detection and diagnosis of electrical motors are of utmost importance in ensuring the safe and reliable operation of several industrial systems. Detection and diagnosis of faults at the incipient stage allows corrective actions to be taken in order to reduce the severity of faults. The existing data-driven deep learning approaches for machine fault diagnosis rely extensively on huge amounts of labeled samples, where annotations are expensive and time-consuming. However, a major portion of unlabeled condition monitoring data is not exploited in the training process. To overcome this limitation, we propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples, which are most informative and harnesses a large amount of available unlabeled data by effectively combining Active Learning and Contrastive Self-Supervised Learning techniques. It consists of a transformer network-based backbone model trained using an advanced nearest-neighbor contrastive self-supervised learning method. This approach empowers the backbone to learn improved representations of samples derived from raw, unlabeled vibration data. Subsequently, the backbone can undergo fine-tuning to address a range of downstream tasks, both within the same machines and across different machines. The effectiveness of the proposed methodology has been assessed through the fine-tuning of the backbone for multiple target tasks using three distinct machine-bearing fault datasets. The experimental evaluation demonstrates a superior performance as compared to existing state-of-the-art fault diagnosis methods with less amount of labeled data.Comment: 30 pages, 2 figures, 7 table

Towards AI enabled automated tracking of multiple boxers

Continuous tracking of boxers across multiple training sessions helps quantify traits required for the well-known ten-point-must system. However, continuous tracking of multiple athletes across multiple training sessions remains a challenge, because it is difficult to precisely segment bout boundaries in a recorded video stream. Furthermore, re-identification of the same athlete over different period or even within the same bout remains a challenge. Difficulties are further compounded when a single fixed view video is captured in top-view. This work summarizes our progress in creating a system in an economically single fixed top-view camera. Specifically, we describe improved algorithm for bout transition detection and in-bout continuous player identification without erroneous ID updation or ID switching. From our custom collected data of ~11 hours (athlete count: 45, bouts: 189), our transition detection algorithm achieves 90% accuracy and continuous ID tracking achieves IDU=0, IDS=0

Persistence of G10P[11] neonatal rotavirus infections in southern India

BACKGROUND: Neonatal rotavirus infections are predominantly caused by distinct genotypes restricted to this age-group and are mostly asymptomatic. METHOD: Stool samples from neonates admitted for >48 h in neonatal intensive care units (NICUs) in Vellore (2014–2015) and Chennai (2015–2016) in southern India, and from neonates born at hospitals in Vellore but not admitted to NICUs (2015–2016) were tested for rotavirus by ELISA and genotyped by hemi-nested RT-PCR. RESULTS: Of 791 neonates, 150 and 336 were recruited from Vellore and Chennai NICUs, and 305 were born in five hospitals in Vellore. Positivity rates in the three settings were 49.3% (74/150), 29.5% (99/336) and 54% (164/305), respectively. G10P[11] was the commonly identified genotype in 87.8% (65/74), 94.9% (94/99) and 98.2% (161/164) of the neonates in Vellore and Chennai NICUs, and those born at Vellore hospitals, respectively. Neonates delivered by lower segment cesarian section (LSCS) at Vellore hospitals, not admitted to NICUs, had a significantly higher odds of acquiring rotavirus infection compared to those delivered vaginally [p = 0.002, OR = 2.4 (1.4–4.3)]. CONCLUSIONS: This report demonstrates the persistence of G10P[11] strain in Vellore and Chennai, indicating widespread neonatal G10P[11] strain in southern India and their persistence over two decades, leading to interesting questions about strain stability

Immune predictors of oral poliovirus vaccine immunogenicity among infants in South India.

Identification of the causes of poor oral vaccine immunogenicity in low-income countries might lead to more effective vaccines. We measured mucosal and systemic immune parameters at the time of vaccination with oral poliovirus vaccine (OPV) in 292 Indian infants aged 6-11 months, including plasma cytokines, leukocyte counts, fecal biomarkers of environmental enteropathy and peripheral blood T-cell phenotype, focused on gut-homing regulatory CD4+ populations. We did not find a distinct immune phenotype associated with OPV immunogenicity, although viral pathogens were more prevalent in stool at the time of immunization among infants who failed to seroconvert (63.9% vs. 45.6%, p = 0.002). Using a machine-learning approach, we could predict seroconversion a priori using immune parameters and infection status with a median 58% accuracy (cross-validation IQR: 50-69%) compared with 50% expected by chance. Better identification of immune predictors of OPV immunogenicity is likely to require sampling of mucosal tissue and improved oral poliovirus infection models

Impact of maternal antibodies and microbiota development on the immunogenicity of oral rotavirus vaccine in African, Indian, and European infants: a prospective cohort study

Identifying risk factors for impaired oral rotavirus vaccine (ORV) efficacy in low-income countries may lead to improvements in vaccine design and delivery. We measured maternal rotavirus antibodies, environmental enteric dysfunction (EED), and bacterial gut microbiota development among infants receiving two doses of Rotarix in India (n = 307), Malawi (n = 119), and the UK (n = 60), using standardised methods across cohorts. ORV shedding and seroconversion rates were significantly lower in Malawi and India than the UK. Maternal rotavirus-specific antibodies in serum and breastmilk were negatively correlated with ORV response in India and Malawi, and this was mediated partly by a reduction in ORV replication. In the UK, ORV replication was not inhibited despite comparable maternal antibody levels. In both India and Malawi, pre-vaccination microbiota diversity was negatively correlated with ORV immunogenicity, suggesting that high early-life microbial exposure may contribute to impaired vaccine efficacy

The effect of probiotics and zinc supplementation on the immune response to oral rotavirus vaccine: A randomized, factorial design, placebo-controlled study among Indian infants.

BACKGROUND: Strategies are needed to improve oral rotavirus vaccine (RV), which provides suboptimal protection in developing countries. Probiotics and zinc supplementation could improve RV immunogenicity by altering the intestinal microbiota and immune function. METHODS: Infants 5weeks old living in urban Vellore, India were enrolled in a randomized, double-blind, placebo-controlled trial with a 4-arm factorial design to assess the effects of daily zinc (5mg), probiotic (1010Lactobacillus rhamnosus GG) or placebo on the immunogenicity of two doses of RV (Rotarix®, GlaxoSmithKline Biologicals) given at 6 and 10weeks of age. Infants were eligible for participation if healthy, available for the study duration and without prior receipt of RV or oral poliovirus vaccine other than the birth dose. The primary outcome was seroconversion to rotavirus at 14weeks of age based on detection of VP6-specific IgA at ?20U/ml in previously seronegative infants or a fourfold rise in concentration. RESULTS: The study took place during July 2012 to February 2013. 620 infants were randomized equally between study arms and 551 (88.9%) completed per protocol. Seroconversion was recorded in 54/137 (39.4%), 42/136 (30.9%), 40/143 (28.0%), and 37/135 (27.4%) infants receiving (1) probiotic and zinc, (2) probiotic and placebo, (3) placebo and zinc, (4) two placebos. Seroconversion showed a modest improvement among infants receiving probiotic (difference between groups 1, 2 and 3, 4 was 7.5% (97.5% Confidence Interval (CI): -1.4%, 16.2%), p=0.066) but not zinc (difference between groups 1, 3 and 2, 4 was 4.4% (97.5% CI: -4.4%, 13.2%), p=0.272). 16 serious adverse events were recorded, none related to study interventions. CONCLUSIONS: Zinc or probiotic supplementation did not significantly improve the low immunogenicity of rotavirus vaccine given to infants in a poor urban community in India. A modest effect of combined supplementation deserves further investigation. TRIAL REGISTRATION: The trial was registered in India (CTRI/2012/05/002677)

An approach to identify the cognitive load on the operator using pupillometry information

by C.Sharma, Babji Srinivasan and Rajagopalan Srinivasa