1 Recommended Practice for Signal Treatment Applied to Smart Transducers P1451.001 Sponsoring Society and Committee: IEEE Industrial Electronics Society/Industrial

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Recommended Practice for Signal Treatment Applied to Smart Transducers

P1451.001

Sponsoring Society and Committee: IEEE Industrial Electronics Society/Industrial Electronics Society Standards Committee (IES/IES)Sponsor Chair: Victor Huang

Joint Sponsor: IEEE Instrumentation and Measurement Society/TC9 - Sensor Technology (IM/ST) Chair Kang Lee- NIST-USA

Working Group Chair – IES: Gustavo Monte (Universidad Tecnologica Nacional, Argentina)Working Group Co-Chair – IMS: Ruqiang Yan (SouthEast Univ, China)


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P1451.001 Development Status

The purpose is to define a standardized and universal framework that allows smart transducers to extract features of the signal being generated and measured. With the definition of these practices, the raw data can be converted into information and then into knowledge. In this context, knowledge means understanding of the nature of the transducer signal. This understanding can be shared with the system and other transducers in order to form a platform for sensoryknowledge fusion.

PURPOSE

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P1451.001 SCOPE

Scope: This recommended practice defines signal processing algorithms and data structure in order to share and to infer signal and state information of an instrumentation or control system.

These algorithms are based on their own signal and also on the transducers attached to the system. The recommended practice also defines the commands and replies for requesting information and algorithms for shape analysis such as exponential, sinusoidal, impulsive noise, noise, and tendency.

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The future of smart sensors and Why we need a standard for sensor signals

Smart Sensor

Data transmission Wired or Wireless Bottleneck (channel capacity, regulations,spectrum,power constrains)

More processing power .For example, The Play Station 3 has the same peak processing (1.8 Teraflops) than the Sandia Lab Supercomputer in 1997.

(IEEE SPECTRUM , February 2011 pag, 48)

In the near future, the sensor signal will be entirely processed into the sensor, even for small and cheap sensors.

Today, This is done but only for complex signal processing related to specific issues. In general, we are wasting processing power.

POM Point Of Measurement (The world is inferred from here)POA Point Of Adquisition

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P1451.001 Development Status

Assuming that the sensor signal will be processed in the POM. What the sensor should report?

DATA

INFORMATION

KNOWLEDGE

BA

ND

WID

TH

Data=> Samples

Information=> Preprocessed data without a particular objective.

Knowledge-=> Feature extraction of particular interest.

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Actuator(Heater)

Temperature SensorTemperature SensorActuator(Heater)

Does your signal exhibit an exponential behavior?

yes

You are validated

Actuator-Sensor dialogue

Brand A Brand B

Thank You!Working properly state

Working properly state

The whole system is validated, actuator OK, temp sensor OK, liquid inside

If something is wrong, we realize of it before a timeout!.

Few bits/sec

EXAMPLE

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P1451.001 Status

PAR Request Date: 18-Oct-2011PAR Approval Date: 06-Feb-2012PAR Expiration Date: 31-Dec-2016

1. WG P&P Working Group Policies and Procedures ( based on IEEE templates)

2. Conform a main WG ( In this case from IES and IMS)

3. Initial Draft document – (Important to establish the ideas that fired the proposal)

4. Call for Participation (IEEE SA) (near 80 participant willing to join)

5. Meeting (WEB based).(aprox every two months). Occasionally face to face meetings.

IES and I&MS are co-sponsors.

(Not common in IEEE SA)

IMPORTANT DATES

STEPS:


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OUTINE OF THE PROPOSED STANDARD

The smart sensor should recognize the evolution of its signal like an observer does.

The sensor should see the “big picture”


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SEGMENTATION ALGORITHM LIN1

SEGMENT CLASSIFICATION ALGORITHM SA

INTRA SEGMENT ANALYSIS

OVERSAMPLED SENSOR SIGNAL

Signal vectorization

Signal abstraction

Feature extraction

To higher order information processing tools ,Neural Networks,

Fuzzy logic,Genetic Algorithms

RTSAL

Algorithm

The proposal is to standardized the sensor signal behavior based on time domain analysis for any kind of sensor.


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a

b

h

g

f

e

d

c

constant

linear +

linear (-)

+exp+

(-)exp+

noisy

c-exp(-)

c+exp(-)

Establish the relationship between tagged samples. This can be seen as a new concept for sampling process.


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OUTLINE OF THE PROPOSED STANDARD

Three vectors describe the signal: (M,C,T)

M (Mark)={ 1.8,1.7,-0.3…..

C (Class)={g,g,e,d,d,g,e….

T (TempPos)={6,8,12,14,………


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DIAGRAM OF THE PROPOSED STANDARD


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OUTLINE OF THE PROPOSED STANDARD: EXPONENTIAL DETECTION

Segments class “e”

Segments class “d”

Segments class “f”

Segments class “g”

INPUT PARAMETERS: •Number of consecutive segments required to start the algorithm.•Number of samples for predicted signal.

OUTPUT PARAMETERS: •Exponential detection (yes/no).•Type of detection. ( four types of exponential shape)•Steady state value.•Predicted signal (yes/no), value.

The steady state value can be predicted


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OUTLINE OF PROPOSED STANDARD: NOISE DETECTION

Noise detectionNoise is detected by computing the distance in

samples among maximums and minimums. If a maximum occurs, then the next one is a minimum and vice versa. If the temporal difference between these two singularities is less than a prefixed value for N times, a noisy flag is turned on.

INPUT PARAMETERS:Maximum amount of samples between max-min-max

to be considered as a noisy signal.Number of consecutive detection of noisy signal.

OUTPUT PARAMETERS:NOISY (yes/no)

a

b

h

g

f

e

d

c

constant

linear +

linear (-)

+exp+

(-)exp+

noisy

c-exp(-)

c+exp(-)

Local minimum at the union of:

fd, fg,ed, eg segments.

Local maximum at the union of

df,de,ge,gf.


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OUTLINE OF PROPOSED STANDARD: IMPULSIVE NOISE DETECTION

An abrupt reduction of the segment length starts the algorithm. This indicates the presence of noise. To be considered as impulsive noise, the change in amplitude must be significant.

Once the algorithm has started, it looks for the first maximum or minimum. Then, it computes the amplitude change from the value before the length reduction.

The length of the impulsive noise is computed from the temporal difference between the first segment and the last one.

For example, the sequence “dddffff” indicates the occurrence of a maximum, and the impulse length is the difference between the temporal position of the last “f” and the first “d”.

INPUT PARAMETERS:Relative amplitude change to be considered as an impulsive noise.OUTPUT PARAMETERS:Detection yes/no.Type of impulse (“df”,”eg”….)Amplitude of impulse.Length of impulse in samples.


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OUTLINE OF PROPOSED STANDARD: SINUSOIDAL PATTERN DETECTION

a

b

h

g

f

e

d

c

constant

linear +

linear (-)

+exp+

(-)exp+

noisy

c-exp(-)

c+exp(-) Dots indicate tagged samples. Class(n)=[ g,g,g,e,e,e,f,f,f,f,g,g].

INPUT PARAMETERS:NoneOUTPUT PARAMETERS:Detection of pattern (yes/no)Estimated Period (samples)

Stable or unstable oscillations.

The sinusoidal patter is composed of the four class segment “gefd” including repetitions of the same class.

For example “gggeeefffdddd” is also a valid sinusoidal pattern. The maximum occurs at the union of “ge” segments and the minimum at “fd”. The period is the difference between singularities of the same kind.

By computing peak to peak values, we can determine if the oscillations are increasing or decreasing.


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1 1minmin

1 1min

( )( )

( )x x x x

x x x x

T T M MTrend

T T M M

T1 T2 T3 T4 Tx

Max 1 Max 2

Max x

Max 3Max 4

1 1maxmax

1 1max

( )( )

( )x x x x

x x x x

T T M MTrend

T T M M

OUTLINE OF PROPOSED STANDARD: TENDENCY ESTIMATION

min max 2

Trend TrendTrend

INPUT PARAMETERS:Windows size in samples.OUTPUT PARAMETERS:Tendency for max.[-1,1]Tendency for min.[-1,1]

Tendency is computed taking into account the increment or decrement of both, the maximums and minimums in a temporal window.

Growing trend is normalized to +1 when all the new maximums are higher than the previous ones in the window.

On the other hand, the trend is decreasing, and normalized to -1, when all the new maximums are lower than the previous ones in the window.

The same procedure is executed for minimums.


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OUTLINE OF PROPOSED STANDARD: MORE RELIABLE VALUE

Since that there is information about the signal shape, it is possible to exclude in the mean estimation of the signal some artifacts. In this algorithm, segments that belong to impulsive noise are discarded in the mean computation.

INPUT PARAMETERS:Window length for mean computation.OUTPUT PARAMETERS:Mean including all the segments.Mean without impulsive noise.

Low pass filtered signal

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OUTLINE OF THE PROPOSED STANDARD: COMPLEX PATTERN DETECTION

Detection of normal QRS complex in ECG signals

Unexpected sequence of segments in a real signal. In this case too many “f” and “d” types.

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COMPLEX PATTERN DETECTION: EEG spike detection with noise

EEG signal (eeg_3_01) from MatLab EEG signal with rand normal noise added

EEg after 3 iterations of RTSAL err=0.001 EEg after 10 iterations of RTSAL err=0.001

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Working Group objectives

The initial draft document just presents the main ideas.

We will keep the smart sampling process (MCT vectors) as a platform for every algorithm in this WG.

•Review and define the proposed algorithms for layer one.

•Propose new algorithms.

•Develop user defined application code based on MCT vectors.

•Propose the data structure.

•Propose a time synchronization scheme.

•Define and propose the interface with the IEEE 1451 standards.

•Propose learning algorithms for specific patterns.

•Propose filtering and signal compression.

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Proposed working group organization

Coordination subgroup IES &IMS

Impulse, noise detection and mean estimation.

Data structure and time synchronization

Tendency, sinusoidal and exponential patterns

New algorithms

Interface with IEEE 1451 Standards

Filtering and signal compression

User defined application code

Learning algorithms for patterns

Learning algorithms for patterns

Testing

Depending on the number of participants the subgroups will work in parallel or sequentially Signal prediction

Subgroups

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Working group organization

We will send to all the participants a brief survey that will help us to define the role in the WG.

Thank you for the interest in this proposal!

Documents

1 Recommended Practice for Signal Treatment Applied to Smart Transducers P1451.001 Sponsoring Society and Committee: IEEE Industrial Electronics Society/Industrial