IEEE 21451-001: Extracting Knowledge from sensor signals, first steps 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) Gustavo Monte Chairman IEEE 21451-001-2017 Universidad Tecnologica Nacional Regional Del Neuquen. ARGENTINA

IEEE 21451-001: Extracting Knowledge from sensor signals, first steps Outline Introduction Information source for algorithms Standard structure Applications Conclusions Process IEEE 21451-001-2017 Recommended Practice for Signal Treatment Applied to Smart Transducers

Introduction: What, How and Where? The four elements that define a network. Depending on the order of execution and the physical location, totally different interconnection networks are generated. Process IEEE 21451-001-2017 Recommended Practice for Signal Treatment Applied to Smart Transducers

Introduction: What, How and Where? The IEEE 21451-001-2017 Focuses on, or propose solutions to: How to Sense? Where to Process? What to Transmit? Process IEEE 21451-001-2017 Recommended Practice for Signal Treatment Applied to Smart Transducers

Introduction: The future of smart sensors and Why we need a standard for sensor signals SCOPE: Sensor networks and IoT DATA Smart Sensor INFORMATION BANDWIDTH KNOWLEDGE Data => Samples Information=> Preprocessed data without a particular objective. Knowledge-=> Feature extraction of particular interest. The lowest data rate is obtained by transmitting questions that require signal processing that have a yes / no response. Not easy to share Standards are mandatory

Sampling, the key for smart sensing Uniform sampling only give me the value IEEE 21451-001-2017 IEEE 21451-001

Sampling, the key for smart sensing The sensor signal is described by the union of known segments Information about the shape is needed from the sampling process Compare signal trajectory against linear IEEE 21451-001-2017

Sampling, the key for smart sensing Give us the necessary information to infer global behavior like an observer does. IEEE 21451-001-2017

Information structure (MCT) 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,……… Executed in real time There is no aliasing since the error subspaces are controlled by the interpolation error IEEE 21451-001-2017

Standard Structure Mark vector Class vector Temp vector Circular Buffer Timestamp Samples Segmentation Algorithm RTSAL Class vector Temp vector FIRST LAYER SECOND LAYER Exponential detection Signal prediction Noise detection MCT Smart filtering and compression Impulsive noise M’C’T’ MCT Sinusoidal pattern MCT Complex pattern detection and learning Tendency estimation MCT Mean estimation User defined application code IEEE 21451-001-2017

First Layer Exponential detection Noise detection Impulsive noise MCT ServiceName Definition (Interface description Language -IDL) None (off) None Initialization Command IDL: UInt16 initialization (in UInt32 samplingPeriod, in UInt8 err, in UInt16 channelID, in UInt32 bufferSize, in UInt16 maxSegLength) Configuration Command IDL: UInt16 configuration(in _Boolean exponentialEnabling, in _Boolean noiseEnabling, in _Boolean impulseEnabling, in _Boolean sinusoidalEnabling, in _Boolean tendencyEnabling, in _Boolean meanReliableEnabling, in UInt8 exponentialAmount, in UInt8 noisyPeriod, in UInt8 amountNoisy, in UInt8 impulseThreshold, in UInt16 tendencyWindow, in UInt8 windowMeanEstimation) RawData IDL: UInt16 readData(in UInt16 n, out UInt16Array data) MCTSegments IDL: UInt16 readMCT(in UInt16 n, out UInt16Array m, out UInt8Array c, out UInt32Array t) ExponentialDetection IDL: UInt16 exponentialDetection(in UInt8 exponentialAmount, in UInt16 samplingPeriod, out UInt8 exponentialType, out UInt16 timeSinceExponential, out _Boolean exponentialStable) FIRST LAYER Exponential detection Noise detection Impulsive noise MCT Sinusoidal pattern Tendency estimation Mean estimation IEEE 21451-001-2017

Information structure void MCT_conversion(void) { float diff = 0; char buffer[20]; int puini, last,pu=0; pu=pu_MCT%NN;   diff=((buffer_samples[pu]+buffer_samples[(pu+k_MCT)%NN])/2-buffer_samples[(pu+k_MCT/2)%NN]); if(diff==0) Equal=Equal+1; last=0; } else if(diff>0) Up=Up+1; // could be segment F or D last=1; else if(diff<0) IEEE 21451-001-2017

Applications Is this signal periodic? Iteration=8, N=14 Signal reconstruction using linear interpolation IEEE 21451-001-2017

Applications The values are normal but not the pattern (Class F ) IEEE 21451-001-2017

Applications Sinusoid detection with noise. FS=22050, f=345 Hz SNR=7dB. Iterations=10. Interpolation error: 1%. Red: original signal. Green: signal reconstructed from MCT vectors. A new set M’C’T’ is obtained. IEEE 21451-001-2017

Applications Filtered of a real ECG signal ,Error=2 lsb, iterations=10 IEEE 21451-001-2017

Applications Classification of defective bearing vibration signals Algorithm based on MCT: Envelope detection using local maximum and variance of envelope Classification of defective bearing vibration signals IEEE 21451-001-2017

Conclusions This standard provides a platform for knowledge sharing. It propose a new sampling technique that removes redundancy while keep the information structure. KNOWLEDGE INFORMATION DATA Technological convergence is requiring standards in unexpected fields IEEE 21451-001-2017

IEEE 21451-001-2017 Thank you!!