Real-Time Anomaly Detection using Average Level-Crossing Rate

Vibration data collected from piezoelectric sensors serve as a means for detecting faults in machines that have rotating parts. The sensor output that is sampled at the Nyquist rate is stored for analysis of faults in the traditional condition monitoring system. The massive amount of data makes the analysis very difficult. Very complex procedures are adopted for anomaly detection in standard methods. The proposed system works on the analog output of the sensor and does not require conventional steps like sampling, feature extraction, classification, or computation of the spectrum. It is a simple system that performs real-time detection of anomalies in the bearing of a machine using vibration signals. Faults in the machines usually create an increase in the frequency of the vibration data. The amplitude of the signal also changes in some situations. The increase in amplitude or frequency leads to a corresponding increase in the level-crossing rate, which is a parameter indicating the rate of change of a signal. Based on the percentage increase in the average value of the level-crossing rate (ALCR), a suitable warning signal can be issued. It does not require the data from a faulty machine to set the thresholds. The proposed algorithm has been tested with standard data sets. There is a clear distinction between the ALCR values of normal and faulty machines, which has been used to release accurate indications about the fault. If the noise conditions do not vary much, the pre-processing of the input signal is not needed. The vibration signals acquired with faulty bearings have ALCR values, ranging from 3.48 times to 10.71 times the average value of ALCR obtained with normal bearing. Hence the proposed system offers bearing fault detection with100% accuracy

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ANALISA PERBANDINGAN LAPORAN KEUANGAN DAN RASIO KEUANGAN SEBAGAI ALAT UKUR KINERJA KEUANGAN PADA KOPERASI KARYAWAN SUCOFINDO (KOPERASI SUCOFINDO JAYA) TAHUN 2009 – 2011

Jurnal Ilmiah Binaniaga ◽

10.33062/jib.v9i2.338 ◽

2019 ◽

Vol 9 (2) ◽

pp. 35

Author(s):

Sri Marti Pramudena

Keyword(s):

Financial Performance ◽

Balance Sheet ◽

Fiscal Year ◽

Percentage Increase ◽

Net Worth ◽

Average Value ◽

Financial Position ◽

Profitability Analysis ◽

Equity Ratio ◽

The Cost

This study aims to determine the financial position and financial performance Cooperative Sucofindo Jaya (KOPSUCOFINDO JAYA) from fiscal year 2009-2011 through a comparative analysis / comparisons and ratio analysis. From the research, the authors obtained a picture that results of the financial position and financial performance of KOPSUCOFINDO JAYA as follows: (1) To Horizontal Analysis of the Balance Sheet shows the overall unfavorable developments as the rise of short-term debt experienced a greater percentage increase than the increase in current assets (2) For Horizontal Analysis of the SHU, SHU in 2010 an increase of 125.38% compared to 2009 and in 2011 increased by 282.47% compared to 2009, but this increase was not followed by a reduction in the burden of cost of goods, especially business and this increase was obtained from the contribution percentage increase in other income. (3) For Vertical Analysis of the Balance Sheet shows that in terms of assets, current assets are assets that make up the largest component but also cause considerable investment value embedded in current assets and also showed asset turnover, receivables turnover and working capital is very low under 1 times. (4) For the SHU Vertical analysis shows that income JAYA KOPSUCOFINDO more than 85% absorbed in the Cost of Goods. (5) For liquidity analysis showed that highly liquid KOPSUCOFINDO JAYA obtain an average value above 400%. (6) For solvency analysis shows that the performance is not good / not solvable because the results of the analysis LITA average of above 95%, Total Debt to Equity Ratio in the top 2.000%, and Net Worth Debt Ratio to average below 4%. (7) For activity ratios indicate that the performance is not good for Turnover of Assets value of 1 times. (8) For the rentability analysis KOPSUCOFINDO JAYA show results for ROA of 0.86% (2009), 1.31% (2010), 1.18% (2011), ROE in 2009 is 14.81%, 26.43% in 2010 and 2011 amounted to 31.11%, for the ROI of 0.56% in 2009, in 2010 was 0.96% and by 0.93% in 2011. (9) For the analysis of profitability, for the analysis of GPM in 2009 amounted to 1.49%, in 2010 of 2.31% and 3.92% in 2011. As for the analysis of NPM in 2009 amounted to 0.97%, in 2010 by 1.70% and by 3.10% in 2011. Keywords: Cooperative Financial Performance, horizontal analysis, vertical analysis, Analysis of Liquidity, Solvency Analysis, Activity Analysis, Profitability Analysis, profitability analysis

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ReRe: A Lightweight Real-Time Ready-to-Go Anomaly Detection Approach for Time Series

2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC) ◽

10.1109/compsac48688.2020.0-226 ◽

2020 ◽

Author(s):

Ming-Chang Lee ◽

Jia-Chun Lin ◽

Ernst Gunner Gan

Keyword(s):

Time Series ◽

Anomaly Detection ◽

Real Time ◽

Detection Approach

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Influence of Features on Accuracy of Anomaly Detection for an Energy Trading System

Sensors ◽

10.3390/s21124237 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4237

Author(s):

Hoon Ko ◽

Kwangcheol Rim ◽

Isabel Praça

Keyword(s):

Anomaly Detection ◽

Real Time ◽

Feature Detection ◽

Trading System ◽

Judgment Accuracy ◽

Energy Trading ◽

Real Time Processing ◽

Detection Model ◽

Time Required ◽

The Relationship

The biggest problem with conventional anomaly signal detection using features was that it was difficult to use it in real time and it requires processing of network signals. Furthermore, analyzing network signals in real-time required vast amounts of processing for each signal, as each protocol contained various pieces of information. This paper suggests anomaly detection by analyzing the relationship among each feature to the anomaly detection model. The model analyzes the anomaly of network signals based on anomaly feature detection. The selected feature for anomaly detection does not require constant network signal updates and real-time processing of these signals. When the selected features are found in the received signal, the signal is registered as a potential anomaly signal and is then steadily monitored until it is determined as either an anomaly or normal signal. In terms of the results, it determined the anomaly with 99.7% (0.997) accuracy in f(4)(S0) and in case f(4)(REJ) received 11,233 signals with a normal or 171anomaly judgment accuracy of 98.7% (0.987).

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