2. The physical System: components in the physical system Flashcards
PHYSICAL SYSTEM COMPONENTS
What are the PHYSICAL SYSTEM COMPONENTS?
Mec- Hydr - Elec-fluid sensors–HMI’s
PHYSICAL SYSTEM COMPONENTS
Examples of control hardware
Control hardware
- Embedded controllers
- PLC
- industrial PC (iPC)
- ‘Smart’ drives
̶ Contains controllers in software
̶ Althought software is ‘virtual’, the control is
part of the Cyber-Physical System (CPS)
What do we mean with process and what with process end result?
Conclucions Physical Systems
SENSORS, SIGNAL CONDITIONING AND MACHINE VISION
What are the virtual sensors, and what is virtual sensing.
Virtual sensing = Combine **physical sensor with a model **to determine non-sensed values
̶ Virtual sensing can be considered an example of a Digital Twin, since it consists of a model fed with live data. Provides additional insight of otherwise unknown parameter (= Use case)
“Virtual Sensors are an example of a DT or DS”
What is Kalman Filter en when to use it
Kalman Filter
What is the state observer - Why do we want to decrease the error
The state observer is the estimation of the input or output of the system–because then the stimation will be more real to the variables you are measuring thats why we use the FEEDBACK CONTROLLER
Karman Filter
What is the optimal state measurement
When you combine the real output and the predicted. The product of this two outputs
What are the components of the eq of the karman filter equation.
3 components of the equation: A Posteriori Estimate = Predict (a priori)+ Updat. Two step proces, Prediction - Update, the update use the a priori states used in the prediction in order to get the a postreori state
So how does the Karman filter works, why is the kalman filter recursive
Because it only needs the estimated state and error covariance matrix from the precious time step and the current measurement
EXTENDED KALMAN FILTER
When do you use a EXTENDED KALMAN FILTER
When you have a non linear system. This is the case when the resulting state distribution may not be Gaussian. And therefore, the Kalman filter algorithm may not converge.
Then you implement the EKF which linearizes the nonlinear function around the mean of the current state estimate. At each time step, the linearization is performed locally and the resulting Jacobian matrices are then used in the prediction and update states of the Kalman filter algorithm
Why do you need a Amplificator
Because the signal of your sensor has to be amplified or also depending on the signal you get out the sensor, other times you has to integrate
SIGNAL CONDITIONING
What is the DIGITAL DATA ACQUISITION proces, 5 steps
What are the analog filters
Digital and analog filters both take out unwanted noise or signal components, but filters work differently in the analog and digital domains. Analog filters will remove everything above or below a chosen cutoff frequency, whereas digital filters can be more precisely programmed
Analog filters are circuits made of analog components such as resistors, capacitors, inductors, and op amps
The continuously varying signals (analog signals) can be operated using passive linear electronic analog filters which are composed of passive elements such as resistors, capacitors, and inductors. These analogue filters are frequently used for allowing particular frequency components by rejecting other from analog or continuous time signals.
What are the high pass and low pass filter
High pass: An analog filter that removes all signals below a certain frequency is a high pass filter, because it lets pass everything higher than the cutoff frequency.
Low pass: Analog filters that remove signals above a certain frequency are called low pass filters because they let low frequency signals pass through the filter while blocking everything above the cutoff frequency.
The order means, when you are adding more filters
What is the cutoff frequency
Cutoff frequency (also known as corner frequency, or break frequency) is defined as a boundary in a system’s frequency response at which energy flowing through the system begins to be attenuated (reflected or reduced) rather than passing through.
The cutoff frequency or corner frequency in electronics is the frequency either above or below which the power output of a circuit, such as a line, amplifier, or electronic filter (e.g. a high pass filter) has fallen to a given proportion of the power in the passband
Most frequently this proportion is one-half the passband power, also referred to as the 3 dB point since a fall of 3 dB corresponds approximately to half power.
Why does Analog filtering is essential?
How is this done?
Why should we sampled?
Digital filters can be more precise in filtering,** but the signal must be digital.** Placing a digital filter in an analog signal chain would require the analog signal to be converted to a digital signal before the digital filtering could be applied and, with any conversion, there are trade-offs in signal integrity. Digital filters work by oversampling and averaging, and are programmable. But it is wise to apply an analog filter prior to signal conversion so that all unwanted frequencies above or below where the desired signal is reasonably expected to operate are removed first.** The conversion process and choosing an analog-to-digital signal converter (ADC) is in itself a careful process that involves choosing a sampling rate that will avoid aliasing during conversion. This is because an aliased signal is indiscernible to a digital filter and therefore the aliased portions of the signal would become a permanent part of the digital signal.
What for is used the Butterworth Filter and what is its main characteristic
The filters are used for shaping the signal’s frequency spectrum. The signal processing filter which is having a flat frequency response in the passband can be termed as Butterworth filter and is also called as a maximally flat magnitude filte
What are the main disadvantages Butterworth Filter
What are time domain signals and what are frequency domain signals
What is a Moving Average Filter
Moving Average Filter is a Finite Impulse Response (FIR) Filter smoothing filter used for smoothing the signal from short term overshoots or noisy fluctuations and helps in retaining the true signal representation or retaining sharp step response. It is a simple yet elegant statistical tool for de-noising signals in the time domain.
These filters are favourite for most Digital Signal Processing (DSP) applications dealing with time-series data. It is simple, fast, and shows amazing results by suppressing noise and retaining the sharp step response. This makes it one of the optimal choices for time-domain encoded signals.
The Moving Average filter is a good smoothing filter in the time domain but a terrible filter in the frequency domain. In applications where only time-domain processing is present Moving average filters shine, but in applications where information is encoded in both time and frequency or in frequency domain solely it can be a terrible option to choose.
Why should we be careful with the Digital Filtering even though its much easier than analog filteren
Because when sampling at lower frequencies you can get aliasing or you dont get the correct shape, thats why you need to sampling at the Niquist frequency. The thumb rule is 10 times the freq but the theorem says freq/2
Why do you even still do analogue filter if you can digital filter
because when you get a signal (coming from a sensor) this can be so high and then if you digitised with the same sample fs (niquist of higher) it can be that you can’t resambled the signal you want to capture. thats why you need a analogue filter to to eliminate the higher freq OR you can sample with a very super high freq and then you can use the **Moving average filter **..You need analogue filtering but be aware that you can sometimes eliminate the information you want. The decission to take is AT WHICH FREQUENCY YOU MUST SAMPLE
ADC
What is the signal granularity
What is the sampling rate you need for a signal and what is the frequency content of the signal; At which frequency you want have meaningful information of your signal…how detailed a signal is,
Machine vision
Which kind of information get from the cameras
Classical ‘discrete’ sensors provide only local info
̶ Camera’s provide rich spatial and temporal information = Gold mine for Digital Twins!
̶ OpenCV: Open source Python machine vision library
What are the ARUCO MARKERS
Are unique identifiers - you can identified the location and the angle. - with the use of marker you can detect the object by a easier way, other wise you always must teach the model by knowing the object
Conclusion Sensors And Signal Conditioning
Sensors provide the required data to bring the model in a Digital Twin alive
̶ Virtual sensing = Form of specific Digital Twin
̶ Machine vision provides gold mine of data for DT
̶ Beware of signal conditioning and granularity!
DIGITAL TWIN IT ARCHITECTURE: LOCAL LEVEL
What are the IT LEVELS
What is the diffrerence between REAL-TIME COMPUTING VS SIMULATION
What is CO-SIMULATION and which other packages or options we have to optimise the DT, knowing that the DT consist of multiple sub-components
Simultaneous computation of multiple software packages
- Useful for multi-physics simulation
- Example: Mechanical dynamics in Siemens NX + Control in Simulink
**Docker **= A piece of software bundled in a self-contained ‘container’
Example: a Simulink model of mechanics, a parameter tracker in
Python, a visualisation in rviz, a signal conditioner in C,
Twincat: This enables deployment of Digital Twin straight
into real-time controller of the physical system!
LOCAL LEVEL IT FOR DT
Data and IT architecture—DIGITAL TWIN DATA ECOSYSTEM
How does the data is interconnected in the DT
OPEN SYSTEMS INTERCONNECTION MODEL
Image of a DT architecture: Check the important aspects and what is synchronization
Many services (including digital twin) require accurate synchronization for correct operation.
For this we rely on the use of highly accurate primary reference clocks which are
distributed network-wide using synchronization links and synchronization supply units
REAL TIME VS DUE TIME, what is DUE TIME
Due time is the time that the data takes to travel in the network, by menas the delays and these can be “Network and Processing”
Data
Objects and Attributes in Data
What are the two kinds of Data?
When measure an attributre this may not match the attributes properties. The type of data and the attribute should be the same BUT the scale not
What are the key message for the attributes
What are the types of data sets, look at the images
What are the most studied dimensions regarding the data quality and what are the quality problems
Correctness - Competeness - Consistency - Time-related dimensions.** Quality problems**.
Noise, wrong data, fake data, missing values and duplicate data
What is data FUSION and what are the important characteristics to consider
