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24/02/2015Data AnalysisScheme of work and assessment plan - updateDateTopicAssessment17:45h - 19:15h19:30h - 21:00h03/02Introduction to UnitFailure and breakdown10/02Failure and breakdownMonitoring24/02Data analysisData analysisOut03/03VibrationUnit 24 Assignment Review10/03Leak detectionCorrosion and Crack detection17/03TemperatureReview24/03ReviewAssignment Review31/03Easter break07/04Easter break12/04Assessment submission deadline (via Moodle)In14/04Fault Analysis starts16/04FeedbackData AnalysisData analysis:computerised systemsdata acquisition techniquesuse of generic computer software (such as spreadsheets, databases)fault analysis/diagnosisplant down time analysisdata storage techniqueshigh-speed data capturetrend analysisexpert systemscondition monitoring integrated within normal plant and machinery controldata acquisition systemsData Analysis

Manual SystemsAvoid unplanned downtime Early failure detection helps avoid unplanned machine downtime, and effectively solves runnability problems. Rolls, bearings, gears and other drive train components produce low-level signals at an early stage when a fault is developing, but is not yet apparent to operators or maintenance personnel.The Sensodec 6S system can immediately detect even these early signs of defects with sensitive high-quality vibration sensors designed for monitoring in a paper machinery environment. Fast measurement cycles, speed-adaptive alarm handling and advanced analysis tools make these signs fully apparent to the personnel. As a result, maintenance actions can be scheduled on time and for the right reasons.

Data AnalysisComputerised Systems

Overview shows that a warning (yellow) in two points in one of the briquetting machines. The 24 month trends show that vibration level in 1000-3000 Hz range has slowly risen compared to the red alarm limit line.

Metso specialist Aarno Kernens analysis of this spectrum showed why the vibration level has increased, there are clear bearing fault harmonics around 1300-1900 Hz.Data Analysis

Data Analysis

Data Analysis

Data Analysis

Data AnalysisSources and systems

Data acquisition begins with the physical phenomenon or physical property to be measured.

Examples of this include temperature, light intensity, gas pressure, fluid flow, and force.

Regardless of the type of physical property to be measured, the physical state that is to be measured must first be transformed into a unified form that can be sampled by a data acquisition system.

The task of performing such transformations falls on devices called sensors.

A data acquisition system is a collection of software and hardware that lets you measure or control physical characteristics of something in the real world. A complete data acquisition system consists of DAQ hardware, sensors and actuators, signal conditioning hardware, and a computer running DAQ software.Data AnalysisA sensor, which is a type of transducer, is a device that converts a physical property into a corresponding electrical signal (e.g., strain gauge, thermistor).

An acquisition system to measure different properties depends on the sensors that are suited to detect those properties.

Signal conditioning may be necessary if the signal from the transducer is not suitable for the DAQ hardware being used. The signal may need to be filtered or amplified in most cases. Various other examples of signal conditioning might be bridge completion, providing current or voltage excitation to the sensor, isolation, linearization.

For transmission purposes, single ended analog signals, which are more susceptible to noise can be converted to differential signals. Once digitized, the signal can be encoded to reduce and correct transmission errors.Data AnalysisA sensor, which is a type of transducer, is a device that converts a physical property into a corresponding electrical signal (e.g., strain gauge, thermistor).

An acquisition system to measure different properties depends on the sensors that are suited to detect those properties.

Signal conditioning may be necessary if the signal from the transducer is not suitable for the DAQ hardware being used. The signal may need to be filtered or amplified in most cases. Various other examples of signal conditioning might be bridge completion, providing current or voltage excitation to the sensor, isolation, linearization.

For transmission purposes, single ended analog signals, which are more susceptible to noise can be converted to differential signals. Once digitized, the signal can be encoded to reduce and correct transmission errors.Data AnalysisData acquisition and control systems need to get real-world signals into the computer. These signals come from a diverse range of instruments and sensors, and each type of signal needs special consideration.

Some data acquisition techniques:Voltage signals (voltage, conditioned transducer, level and flow measurement)High impedance probes (concentration measurement)Current signals (current and conditioned transducer measurement)Power signals (power supply, current and voltage measurement)Thermocouples (temperature measurement)Resistance (temperature, displacement and light level measurement)Strain gauge bridges (strain measurement)Excitation (force, pressure, relative humidity, temperature, level, light level, concentration and vibration measurement)LVDTs (displacement measurement)Encoders (angular position measurement)Counter-Timers (speed and flow measurement)Digital signals (on/off measurement)Data acquisition softwareData Analysis

Analog I/O card:Up to 64 Input Channels per BoardProgrammable Sampling Rates to 50M SPSGPS SynchronizationAuto-CalibrationMulti-Board SynchronizationSigma-Delta and Delta-Sigma Analog I/OResolutions from 12 bits to 24 bitsIEPE Compatibility Data AnalysisManual Data Acquisition (Measurements)

Automatic Data Acquisition

Analog-to-Digital Converters (ADCs) transform an analog voltage to a binary number (a series of 1s and 0s), and then eventually to a digital number (base 10) for reading on a meter, monitor, or chart.

Accuracy vs. Resolution of ADCs

ADC Accuracy vs. System Accuracy

Data Analysis

The straight line in each graph represents the analog input voltage and the perfect output voltage reading from an ADC with infinite resolution. The step function in Graph A shows the ideal response for a 3-bit ADC. Graphs B, C, D, and E show the effect on ADC output from the various identified errors.Data AnalysisSamplingNyquist Theorem: Transforming a signal from the time domain to the frequency domain requires the application of the Nyquist theorem. The Nyquist sampling theorem states that if a signal only contains frequencies less than cutoff frequency, fc, all the information in the signal can be captured by sampling it at a minimum frequency of 2 fc. This means that capturing a signal with a maximum frequency component of fmax requires that it must be sampled at 2 fmax or higher.

However, common practice dictates that while working in the frequency domain, the sampling rate must be set more than twice and preferably between five and ten times the signals highest frequency component.

Data AnalysisSampling

Data AnalysisSignal Filtering

Data AnalysisData AnalysisDigital Filtering

Data Acquisition TechniquesCase Study

Data Acquisition TechniquesCase Study

Data Acquisition TechniquesCase Study

Data Acquisition TechniquesCase StudyData AnalysisData analysis:computerised systemsdata acquisition techniquesuse of generic computer software (such as spreadsheets, databases)fault analysis/diagnosisplant down time analysisdata storage techniqueshigh-speed data capturetrend analysisexpert systemscondition monitoring integrated within normal plant and machinery controldata acquisition systemsData AnalysisData analysis:computerised systemsdata acquisition techniquesuse of generic computer software (such as spreadsheets, databases)fault analysis/diagnosisplant down time analysisdata storage techniqueshigh-speed data capturetrend analysisexpert systemscondition monitoring integrated within normal plant and machinery controldata acquisition systemsData AnalysisData analysis:computerised systemsdata acquisition techniquesuse of generic computer software (such as spreadsheets, databases)fault analysis/diagnosisplant down time analysisdata storage techniqueshigh-speed data capturetrend analysisexpert systemscondition monitoring integrated within normal plant and machinery controldata acquisition systems