deep-learning-based pipe leak detection using image-based ... - SigPort

DEEP-LEARNING-BASED PIPE LEAK DETECTION USING IMAGE-BASED LEAK FEATURES Ji-Hoon Bae1, Doyeob Yeo1, Doo-Byung Yoon2, Se Won OH1, Gwan Joong Kim1, Nae-Soo Kim1, and Cheol-Sig Pyo1, ETRI1, KAERI2, Rep. of Korea

INTRODUCTION Frequency domain H: 2.0 mm, P: 4 bar

t [sec]

Absolute magnitude

Amplitude level

H: 1.0 mm, P: 2 bar

Amplitude level

Time domain

t [sec]

H: 1.0 mm, P: 2 bar

Absolute magnitude

• Recently, in the field of plant industry, corrosion and leakage of pipes have been observed because of aging pipelines installed at the time of initial construction  social problems, economic loss, and human injuries. • We consider acoustic-based leak detection technology (high leak-detection speed and ease of retrofitting). • Characteristics of leakage signals: 1) the magnitude of the measured leakage signal tends to increase with the increase in leakage scales (time-domain) and 2) the magnitude of the frequency spectrum also tends to increase as the leakage scales increase (frequency-domain). • Main objective: implement deep-learning-based pipe leak detection (PLD) using trajectorybased image features that reflect the aforementioned characteristics of the leakage signal.

H: 2.0 mm, P: 4 bar

f [kHz]

f [kHz]

PROPOSED IMAGE FEATURE EXTRACTION Generation of RMS-pattern image feature 1) For path-A, directly calculate the RMS level of the sampled input data x using a moving averaging window. 2) For path-B, calculated the RMS level of the preprocessed signal vector using the band-pass filter and amplitude weighing blocks. 3) Concatenate the two RMS-level feature vectors as . 4) Generate a 2D RMS-pattern image feature from  2D domain is discretized by a MN grid (M-levels in the time range and N-levels in the amplitude range).  The amplitude values of the are quantized with the same Nlevels with respect to the corresponding M-intervals.  Each quantized value is mapped to the corresponding lattice in the 2D MN domain. Small-scale Leak signal

Small-scale noise Large-scale Leak signal

Generation of frequency-pattern image feature 1) Perform a band-pass filtering for the sampled incoming data x. 2) Convert the filtered time-series acoustic data into frequency-domain data using 1D Fourier transform. 3) Generate a 2D frequency-pattern image feature from .  2D domain is discretized by a MN grid (M-levels in the frequency range and N-levels in the amplitude range)  The amplitude values of the normalized absolute values of the are quantized with the same N-levels with respect to the corresponding M-intervals.  Each quantized value is mapped to the corresponding lattice in the 2D MN domain Small-scale Leak signal

Large-scale Leak signal

Small-scale noise

Large-scale noise

Large-scale noise

DEEP-LEARNING-BASED PIPE LEAK DETECTION (PLD) Laboratory facility environment for PLD    

Four 20KHz-microphone sensors. Sampling rate of 150 KHz for 0.5 [s]. 100 test positions of leak test region. 25 combinations of test conditions Hi and Pj (i = 1,2,…,5, and j = 1,2,…,5). Leak scale Values Leak hole 0.5, 1.0, 1.5, 2.0, 2.5 Diameter [mm] Escaping air 1, 2, 3, 4, 5 Pressure [bar]

• • •

Multiple microphone sensor nodes are used to simultaneously collect time-series data of the leakage signals. Construct the RMS volume feature using four RMS-pattern images extracted from the corresponding four sensor nodes. Construct the Frequency volume feature using four frequencypattern images extracted from the corresponding four sensor nodes.

Ensemble deep-learning structure for PLD • 8-layer residual network5 (ResNet) model is used as the base ensemble deep-learning (EDL) for PLD. • Training dataset ( H i Pj i=1with j=1, 3, and 5): 3,000 40404 volume features (1,500 for leakage signals and 1,500 for noises). 5 • Test dataset ( Hi Pj i=1with j=2 and 4): 2,000 40404 volume features (1,000 for leakage signals and 1,000 for noises). • Batch size of 100, training epochs of 33, and momentum optimizer.

Classification accuracy [%] for PLD Accuracy [%]

Method

Epochs

NN using 1160 RMS feature

77.805

88

NN using 1160 frequency feature

98.415

37

LSTM using 40 sequential length

V 3D  3D 8-layer ResNet using 40404 Vrms 8-layer ResNet using 40404

8-layer ResNet using 40404 V 3D 3D and EDL using Vrms V freq

3D freq

84.53

33

80.755

33

99.875

33

99.57

33

99.97

33