After the real time data is collected from the sensors, the first important step for condition monitoring and faults detection is to develop efficient signal analysis algorithm that can be used to extract useful information from raw data for further diagnostic and prognostic purpose. Several useful algorithms that widely used for faults detection purpose will be discussed in this section which includes time-domain analysis, frequency domain analysis, time-frequency analysis and wavelet transform. These techniques are the fundamental of health monitoring methods. Time-domain analysis Most of the signals from sensors are in time series form for example vibration signal, temperature signal and current signal of an induction motor. Time-domain …show more content…
The signals after TSA indicate the feature information related to the faults which need to be detected. The TSA is usually applied to detect faults with known repetitive frequency of desired signal (Mcfadden and Toozhy 2000). For example, faults in rolling bearings, shaft and gears with known defect frequencies. TSA is given by follow equation: Where s ̅(t) represents the signal, T is the averaging period and N is the number of samples for averaging. Frequency-domain analysis Frequency-domain analysis is the most common signal processing algorithm used for online condition monitoring which is based on the transformed signal in frequency domain. Many electrical and mechanical faults of motor generate “noise” signals at other frequencies which can be determined from knowledge of motor parameters. These fault signals in presence of large noise appear in different sensor signals such as current, vibration and flux. By using frequency analysis, different types of faults information can be provided. Because some faults results in similar faults frequencies, to differentiate them, other information is required. Compared with time-domain analysis, frequency-domain analysis is easier to identify and isolate certain frequency components of interest. The fast Fourier transform (FFT) (Burgess 1988) is the most widely used conventional diagnosis technique to
A composite signal can be decomposed into individual sine waves called harmonies.Fourier analsis is done to decompose a signal.the decomposed signals have different amplitude,frequency and phase.A periodic signal has
The average length in the sample size can be determined by adding the lengths and dividing them by the total number of samples 10+10.3+10.2+10.1+10+10+10.2+10+9.9+10+9.8+10+10.8+10.6+9.7/15=10.11
Time Weighted Average is considered the mean exposure by a certain hazard contaminant. Most times this is used for a place of work . An 8 hour time is usually set a standard or baseline time for a day, a short term exposure limit of 15 minutes is also used at times. The equation below is used to calculate the value;
The model-based fault detection approach employs a mathematical model of the system under observation, by assuming that a fault in the system will lead to deterministic changes in the model parameters. The model-based approach relies on comparing the model outputs with the actual system outputs to generate a residual signal, and based upon the properties of the generated residual signal, potential fault conditions are identified and useful information is extracted (Ding 2008). The basic concept of a typical model-based fault detection approach is illustrated in Fig. As indicated in Fig., there are two main stages in this approach, the first of which generates the residual which is then passed to the residual evaluation stage. Throughout the fault-free operation, the magnitude of the residual signal should be approximately zero, indicating that the proposed model is accurately describing the current behavior of the system. If, however, the value of the residual signal diverges from zero, appropriate processing and analysis techniques are applied to it in
There are multiple causes of an accident or a loss-making event. The Fault Tree Analysis is one of the many analytical techniques that is used for tracing the series of events that could contribute to an accident (NEBOSH, 2010).
The first example is the monitoring of the Tsing Ma Bridge and Kap Shui Mun Bridge in Hong Kong . A large amount of sensors include temperature sensors, strain gauges, displacement, anemometer, and accelerometers were installed on the bridge. These sensors were installed to monitor the bridge dynamic response in terms of displacement, acceleration, wind, and temperature. Approximately 320 channels of sensors were used in Tsing Ma Bridge .These sensors produced about 65 M Bytes of data every hour in an attempt toward in-depth monitoring. This SHM system was able to monitor the structural health of the bridge and provide reliable information for civil engineers to plan accordingly for inspection visits.
• • • • • • • • • • • • OR Gate (a): used when output event occurs when one or more input events occur. AND Gate (b): used when output event occurs when all input events occur. Priority AND Gate (c): like AND gate but input events occur in a specified order. Exclusive OR Gate (d): used when output occurs when one and only one of the input events occur. Delay Gate (e): used when output event occurs after a specified time delay. Inhibit Gate (f): used when output event occurs based on a conditional event occurring. M-out-of-N Gate (g): used when output event occurs based on a m out of n input events occurring. Resultant Event (h): used to represent an event resulting from some combination of preceding fault events. Basic Fault Event (i): used to represent failure of component or subsystem. Incomplete Event (j): used to represent a fault event whose cause has not yet been determined. Conditional Event (k): used to represent the
In this review, three different analysis techniques will be discussed, including Structured What If Technique (SWIFT), Failure Modes, Effects, and Diagnostic Analysis (FMEDA) and Technique for Human Error-rate Prediction (THERP). Each of them will be discussed in three aspects (concept, suitable situations and application).
Abstract— A step towards the research of wireless time-triggered networks was taken with the development of a new safety-critical wireless protocol called the Time-Triggered New Zigbee (TTNZ) protocol. The methodology for developing the TTNZ protocol involved Model Driven Engineering (MDE) and a tool called TimeMe which is an IEC 61499 standard compliant software. MDE overcomes the limitations of the traditional approach which does not guarantee consistent performance during the design and development phases of a Wireless Sensor Network (WSN) . This safety-critical wireless protocol, TTNZ, does not take into consideration the irregularities of the physical (PHY) layer. The current project is undertaken to consider the issue of timeliness with PHY layer abnormalities and accordingly use a measurement or testing based approach to verify timeliness.
Abstract— clean air is a basic necessity for all human beings living on earth, yet about 80% of world’s population breathes air that has pollutants which exceed the World Health Organization’s recommended level. Environmental air pollution has significant influence on the concentration of constituents in the atmosphere leading to effects like global warming and acid rains. To avoid such adverse imbalances in nature, an air pollution measuring system is of utmost importance. This paper presents fault detection and isolative model implemented in a novel fault tolerant microcontroller based data acquisition system design for air pollutant concentration measurement in industrial facilities in Nigeria. A supervisory control unit was incorporated in the data acquisition system design in an attempt at robust control. The model utilizes weighted standard deviation /data variance method to detect anomalies and correlation in measured data, with a view to data intelligence elicitation, an error residual generator was designed to handle fault isolation and accommodation. The overall system implements corrective procedures in the event of a fault or failure with appropriate indicators showing in-process operation. Microcontrollers, gas sensors, capacitors, resistors, connecting wires, transistors, voltage regulator, transformer, Light
The possible data collection using the on-board diagnostics tool can be categorized into different groups that may be of use to a variety of applications. Among all the details, most of the data which is relevant for used-car market would be the trip data that gets populated through the entire trip or usage of the vehicle. This covers the details of “trip duration, total kilo-meters travelled, kilo-meters travelled related to the type of road (say: regular motorways, urban areas, bumpy terrain, elevation, other types of roads), travel by time of day (rush hour, daylight, night), acceleration, deceleration and sharp turns” (Vaia, G. (2012)). The in-built setup of OBD device will generate all this information through