In this context of changing and challenging market requirements, Gas Insulated Substation GIS has found a broad range of applications in power systems for more than two decades because of its high reliability, easy maintenance and small ground space requirement etc.
Recent advances in machine learning and cloud storage have created a tremendous opportunity to utilize the gamut of data coming from factories, buildings, machines, sensors, and more to not only monitor equipment health but also predict when something is likely to malfunction or fail.
However, as simple as it sounds in principle, in reality it is often hard to come by all the data that is necessary to actually make such predictions and do so in a timely manner.
In the realm of predictive maintenance, the event of interest is an equipment failure.
In real scenarios, this is usually a rare event. Unless the data collection has been taking place over a long period of time, the data will have very few of these events or, in the worst case, none at all. Ideally, the data should have hundreds or even thousands of failures.
But even in these cases the distribution or the ratio of failure to non-failure data is highly skewed.
Additionally, the data should be collected from all of the relevant parts and should capture the complete picture or timeline of events prior to the occurrence of the failure. Collecting partial information leads to incomplete learning and an imprecise prediction.
For example, if we wanted to predict when the car brakes will fail, we should collect data not just from the brake pads, Failure prediction algorithms essay also from the wheels, the complete maintenance record of the car, when the wheels were replaced, when the brake pads were replaced, the make and model of the car, when it was purchased, the history of how and where the car was driven and more; and over a long period of time.
A model that learns from rich data like this will be able to find patterns and might identify dependencies that would otherwise not be so obvious and correctly predict in advance when a brake failure will occur. As the field matures and there is more understanding around the art of machine learning, businesses will start collecting data more strategically.
Modeling Imbalanced Data Modelling for Predictive Maintenance falls under the classic problem of modelling with imbalanced data when only a fraction of the data constitutes failure. This kind of data poses several issues.
While normal operations data i. Standard methods for feature selection and feature construction do not work so well for imbalanced data. Moreover, the metrics used to evaluate the model can be misleading. This model however is useless as it never learned to predict a failure. More appropriate metrics for evaluating these types of models are precision, recall, AUC etc.
Instead of conventional accuracy, the accuracy per class should be computed and the mean of these accuracies should be reported. For details on how to compute these evaluation metrics, see here. When building models, a clear understanding of the business requirement and the tolerance to false negatives and false positives is necessary.
For some businesses, failure to predict a malfunction can be detrimental e. They would rather the model errs on the side of caution as it is more cost effective to do a maintenance checkup in response to a false prediction rather than a full blown shutdown.
On the other hand, falsely predicting a failure when there is none can be a problem for other businesses due to loss of time and resources to address a falsely predicted failure, in which case the model should be tuned for a high precision. In the language of statistics, this is what we call misclassification cost.
The actual dollar amount associated with a false prediction can be evaluated by the business by taking into account the repair costs, from parts as well as labor, quantifying the effect on their brand and reputation, customer satisfaction etc. This should be the driving factor for tuning the model for cost-sensitive learning.
There are several ways we can circumvent the problems of modelling with imbalanced data. Below I will briefly describe three ways to deal with imbalanced data within the Azure ML framework. Hence the first step is to balance the dataset through resampling.
There are various sampling techniques available, each with their own advantages and disadvantages. You can find a brief description here.Prior attempts of executing algorithms have failed because of the massive bandwidth necessary to process the information in real time.
The calculations in this research will not require many resources and will use simple gate-logic and statistical information to alert an outcome of a potential failure. You will get $40 trillion just by reading this essay and understanding what it says.
For complete details, see below. (It’s true that authors will do just about anything to . breaker failure detection algorithms essay - In a power system world, breaker failure protection became a critical element to provide a back up protection for circuit breakers (CBs).
Practically, every apparatus is equipped with primary protection to interrupt the current flow whenever a fault occurs.
Machine Bias There’s software used across the country to predict future criminals. And it’s biased against blacks. by Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner, ProPublica May. The earliest instances of what might today be called genetic algorithms appeared in the late s and early s, programmed on computers by evolutionary biologists who were explicitly seeking to model aspects of natural evolution.
Keywords: hard drive failure prediction, rank-sum test, support vector machines (SVM), exact nonparametric statistics, multiple instance naive-Bayes 1. Introduction We present a comparison of learning methods applied to a difﬁcult real-world pattern recognition problem: predicting impending failure in hard disk drives.