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  1. Fulltext Detecting industrial motor faults with current signatures
    Krishnan S, Vengadasalam V
    F1000Res, 2021;10:903.
    PMID: 36398279 DOI: 10.12688/f1000research.54266.1
    Background: A major player in industry is the induction motor. The constant motion and mechanical nature of motors causes much wear and tear, creating a need for frequent maintenance such as changing contact brushes. Unmannered and infrequent monitoring of motors, as is common in the industry, can lead to overexertion and cause major faults. If a motor fault is detected earlier through the use of automated fault monitoring, it could prevent minor faults from developing into major faults, reducing the cost and down-time of production due the motor repairs. There are few available methods to detect three-phase motor faults. One method is to analyze average vibration signals values of V, I, pf, P, Q, S, THD and frequency. Others are to analyze instantaneous signal signatures of V and I frequencies, or V and I trajectory plotting a Lissajous curve. These methods need at least three sensors for current and three for voltage for a three-phase motor detection. Methods: Our proposed method of monitoring faults in three-phase industrial motors uses Hilbert Transform (HT) instantaneous current signature curve only, reducing the number of sensors required. Our system detects fault signatures accurately at any voltage or current levels, whether it is delta or star connected motors. This is due to our system design, which incorporates normalized curves of HT in the fault analysis database. We have conducted this experiment in our campus laboratory for two different three-phase motors with four different fault experiments. Results: The results shown in this paper are a comparison of two methods, the V and I Lissajous trajectory curve and our HT instantaneous current signature curve. Conclusion: We have chosen them as our benchmark as their fault results closely resemble our system results, but our system benefits such as universality and a cost reduction in sensors of 50%.
  2. Impact of Diabetes Medication Therapy Adherence Clinic (DMTAC) appointment intervals on glycemic control in public health clinics across Perak, Malaysia
    Beh YS, Gopalsamy K, Lee SLF, P Vengadasalam VP
    Malays Fam Physician, 2022 Nov 30;17(3):105-113.
    PMID: 36606164 DOI: 10.51866/oa134l
    INTRODUCTION: Frequent diabetes medication therapy adherence clinic (DMTAC) appointments may lead to more rapid glycaemic control. This study aimed to evaluate the association between appointment intervals and glycaemic control (haemoglobin A1c [HbA1c] level) along with blood pressure (BP) and lipid profile (LP) during DMTAC appointments.

    METHOD: This study retrospectively reviewed all recorded baseline and completed DMTAC data, including HbA1c level, LP and BP, of 318 eligible participants from 29 DMTACs across Perak. The participants were divided into shorter appointment interval (SAI) (≤30 days) and longer appointment interval (LAI) groups.

    RESULTS: The majority of the baseline socio-demographic and clinical characteristics did not significantly differ between the SAI and LAI groups (p>0.05). Ischaemic heart disease (Odds ratio, OR=3.457; 95% CI= 1.354-8.826; p=0.009) and hypertension (OR=0.521; 95% CI=0.276-0.992; p=0.044) were significantly associated with the appointment intervals. Upon completion of eight DMTAC visits, the HbA1c and FBS levels and DBP significantly improved (p<0.05). However, the mean HbA1c level (1.35±2.18% vs 0.87±2.11%, p=0.548), FBS level (1.25±4.82mmol/L vs 2.29±6.23mmol/L, p=0.538), SBP (3.28±21.82mmHg vs 3.65±18.35mmHg, p=0.343) and LDL level (0.09±0.98mmol/L vs 0.07±1.13mmol/L, p=0.246) did not significantly differ between the SAI and LAI groups.

    CONCLUSION: Longer DMTAC appointment intervals had similar improvement in glycaemic controls, blood pressure and lipid profiles as compared to shorter appointment intervals. A longer interval can be scheduled for lower-risk patients to optimise the use of human resources and minimise costs.

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