Robust detection of incipient faults in VSI-fed induction motors using quality control charts.

A considerable amount of papers have been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper a new method is proposed which is based on robust statistical techniques applied in Quality Control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked, broken bar, a faulty bearing and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults

A Variable Sampling Interval Multivariate Exponentially Weighted Moving Average Control Chart Based on Median Time-to-Signal

In this study, the median time-to-signal (MTS) is used as an alternative measure to the average time-to-signal (ATS) in evaluating the performance of the variable sampling interval (VSI) multivariate exponentially weighted moving average (MEWMA) chart. Although the ATS is one of the most commonly used performance measures when the sampling interval is varied, it is not an accurate representation of the entire time-to-signal distribution of the VSI charts. Therefore, the percentage points (percentiles) of the time-to-signal distribution are provided for a more comprehensive study of the VSI MEWMA chart. A Monte Carlo simulation is used to calculate the MTS values for various magnitudes of shifts in the process mean vector. The optimal design strategy is to find the charting parameters having the minimum out-of-control MTS (MTS1). A comparison study shows that the VSI MEWMA chart is more effective than the standard MEWMA chart with fixed sampling interval, in detecting shifts in the process mean vector in terms of the MTS

Run Rules-Based EWMA Charts for Efficient Monitoring of Profile Parameters

In usual quality control methods, the quality of a process or product is evaluated by monitoring one or more quality characteristics using their corresponding distributions. However, when the quality characteristic is defined through the relationship between one or more response and independent variables, the regime is referred to as profiles monitoring. In this article, we improve the performance of the Exponentially Weighted Moving Average Range (EWMAR) control charts, which are implemented for monitoring linear profiles (i.e., intercept, slope and average residual between sample and reference lines) by integrating them with run rules in order to quickly detect various magnitudes of shifts in profile parameters. The validation of the proposed control chart is accomplished by examining its performance using the average run length (ARL) criteria. The proposed EWMAR chart with run rules exhibits a much better performance in detecting small and decreasing shifts than the other competing charts. Finally, an example from multivariate manufacturing industry is employed to illustrate the superiority of the EWMAR chart with run rules. 2013 IEEE.Scopu

Risk-Based X-bar chart with variable sample size and sampling interval

Flexibility is increasingly important in production management, and adaptive control charts (i.e., control charts with variable sample size and/or variable sampling interval) have significant importance in the field of statistical process control. The value of the variable chart parameters depends on the detected process parameters. The process parameters need to be estimated based on observed values; however, these values are distorted by measurement uncertainty. Therefore, the performance of the method is strongly influenced by the precision of the measurement. This paper proposes a risk-based concept for the design of an X-bar chart with variable sample size and sampling interval. The optimal set of the parameters (control line, sample size and sampling interval) is determined using genetic algorithms and the Nelder-Mead direct search algorithm to minimize the risks arising from measurement uncertainty

A Quality Systems Economic-Risk Design Theoretical Framework

Quality systems, including control charts theory and sampling plans, have become essential tools to develop business processes. Since 1928, research has been conducted in developing the economic-risk designs for specific types of control charts or sampling plans. However, there has been no theoretical or applied research attempts to combine these related theories into a synthesized theoretical framework of quality systems economic-risk design. This research proposes to develop a theoretical framework of quality systems economic-risk design from qualitative research synthesis of the economic-risk design of sampling plan models and control charts models. This theoretical framework will be useful in guiding future research into economic risk quality systems design theory and application

Monitoring non-parametric profiles using adaptive EWMA control chart

To monitor the quality of a process in statistical process control (SPC), considering a functional relationship between a dependent variable and one or more independent variables (which is denoted as profile monitoring) is becoming an increasingly common approach. Most of the studies in the SPC literature considered parametric approaches in which the functional relationship has the same form in the in-control (IC) and out-of-control (OC) situations. Non-parametric profiles, which have a different functional relationship in the OC conditions are very common. This paper designs a novel control chart to monitor not only the regression parameters but also the variation of the profiles in Phase II applications using an adaptive approach. Adaptive control charts adjust the final statistic with regard to information of the previous samples. The proposed method considers the relative distance of the chart statistic to the control limits as a tendency index and provides some outcomes about the process condition. The results of Monte Carlo simulations show the superiority of the proposed monitoring scheme in comparison with the common non-parametric control charts. 2022, The Author(s).The publication of this article was funded by Qatar National Library.Scopu

Analysis of artificial intelligence in industrial drives and development of fault deterrent novel machine learning prediction algorithm

Industrial sectors rely on electrical inverter drives to power their various load segments. Because the majority of their load is nonlinear, their drive system behaviour is unpredictable. Manufacturers continue to invest much in research and development to ensure that the device can resist any disturbances caused by the power system or load-side changes. The literature in this field of study depicts numerous effects caused by harmonics, a sudden inrush of currents, power interruption in all phases, leakage current effects and torque control of the system, among others. These and numerous other effects have been discovered as a result of research, and the inverter drive has been enhanced to a more advanced device than its earlier version. Despite these measures, inverter drives continue to operate poorly and frequently fail throughout the warranty term. This failure analysis is used as the basis for this research work, which presents a method for forecasting faulty sections using power system parameters. The said parameters were obtained by field-test dataset analysis in industrial premises. The prediction parameter is established by the examination of field research test data. The same data are used to train the machine learning system for future pre-emptive action. When exposed to live data feeds, the algorithm may forecast the future and suggest the same. Thus, when comparing the current status of the device to the planned study effort, the latter provides an advantage in terms of safeguarding the device and avoiding a brief period of total shutdown. As a result, the machine learning model was trained using the tested dataset and employed for prediction purposes; as a result, it provides a more accurate prediction, which benefits end consumers rather than improving the power system\u27s grid-side difficulties

Políticas de amostragem em controlo estatístico da qualidade

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Statistics and EconometricsNesta Dissertação apresentam-se e estudam-se, de uma forma crítica, dois novos métodos de amostragem adaptativa e uma nova medida de desempenho de métodos de amostragem, no contexto do controlo estatístico da qualidade. Considerando como base uma carta de controlo para a média do tipo Shewhart, estudamos as suas propriedades estatísticas e realizamos estudos comparativos, em termos do seu desempenho estatístico, com alguns dos métodos mais referenciados na literatura.Inicialmente, desenvolvemos um novo método adaptativo de amostragem no qual os intervalos entre amostras são obtidos com base na função densidade da distribuição de Laplace reduzida. Este método revela-se, particularmente, eficiente na deteção de moderadas e grandes alterações da média, pouco sensível à limitação do menor intervalo de amostragem e robusto face a diferentes situações consideradas para a não normalidade da característica da qualidade. Em determinadas situações, este método é sempre mais eficiente do que o método com intervalos de amostragem adaptativos,dimensões amostrais fixas e coeficientes dos limites de controlo fixos. Tendo como base o método de amostragem definido no ponto anterior e um método no qual os intervalos de amostragem são definidos antes do início do controlo do processo com base na taxa cumulativa de risco do sistema, apresentamos um novo método de amostragem que combina o método de intervalos predefinidos com o método de intervalos adaptativos. Neste método, os instantes de amostragem são definidos pela média ponderada dos instantes dos dois métodos, atribuindo-se maior peso ao método adaptativo para alterações moderadas (onde o método predefinido é menos eficaz) e maior peso ao método predefinido nos restantes casos (onde o método adaptativo é menos eficaz). Desta forma, os instantes de amostragem, inicialmente calendarizados de acordo com as expectativas de ocorrência de uma alteração tomando como base a distribuição do tempo de vida do sistema, são adaptados em função do valor da estatística amostral calculada no instante anterior. Este método é sempre mais eficiente do que o método periódico clássico, o que não acontece com nenhum outro esquema adaptativo, e do que o método de amostragem VSI para alguns pares de amostragem, posicionando-se como uma forte alternativa aos procedimentos de amostragem encontrados na literatura. Por fim, apresentamos uma nova medida de desempenho de métodos de amostragem. Considerando que dois métodos em comparação têm o mesmo tempo médio de mau funcionamento, o desempenho dos métodos é comparado através do número médio de amostras recolhidas sob controlo. Tendo em conta o tempo de vida do sistema, com diferentes taxas de risco, esta medida mostra-se robusta e permite, num contexto económico, um melhor controlo de custos por unidade de tempo

Modeling and designing control chart for monitoring time-between events data

Ph.DDOCTOR OF PHILOSOPH