Transforming Bridge Monitoring from Time-Based to Predictive Maintenance using Acoustic Emission MEMS Sensors and Artificial Intelligence

Thomas R. Hay1, D.R. Hay1 Joel R. Hay2, David W. Greve3, Irving J. Oppenheim3, 1WavesInSolids LLC, State College, USA; 2TISEC Inc., Montreal, Canada; 3Carnegie Mellon

University, Pittsburgh, USA. Abstract The objective of the work presented in this paper is to advance the state of the art in steel bridge safety critical structural monitoring using MEMS acoustic emission sensors and artificial intelligence. By advancing the state of the art, it is the authors’ objective to transform conventional bridge monitoring from a periodic time-based approach to a more sophisticated approach consistent with preventative maintenance philosophies. Three components are presented in this paper. The first component summarizes the current state of the art in steel bridge AE testing. The second component discusses research and results on embedding MEMS AE sensors on steel bridges. The third component presents research and results on applying artificial intelligence to AE bridge data to manage large data rates and volumes efficiently. The envisioned technology would be installed permanently or over long periods on steel bridges to provide feedback on structural integrity in real-time. Introduction As the United States transportation infrastructure, of which bridges, both highway and rail, are an integral part, ages and components and systems reach the end of their economic lives, replacement costs and life extension become important. Throughout this infrastructure, inspection-based maintenance plays a key role in determining its fitness for service. While fail-safe design and maintenance does not tolerate any known crack or crack-like defect, retirement-for-cause recognizes that flaws may exist that do not compromise a structure’s ability to perform its function. The latter approach, developed in the aerospace industry, is now used in the civil and industrial infrastructure with major financial implications. Cost savings result by avoiding unnecessary replacement of sound components and by detecting defective parts before the end of their design life. Inspection is the basis for the retirement-for-cause approach. Structure-generated and -borne acoustic waves contain valuable information on the integrity of materials of construction. In addition to an inherently high sensitivity, acoustic waves propagate through the structure thereby providing remote monitoring of inaccessible areas and the ability to monitor with a minimum of sensors. This is in contrast to most other nondestructive testing methods that require point-by-point inspection and are therefore limited to accessible areas of a structure. Current State of the Art-in-AE Bridge Testing Railways have depended primarily on visual inspection to assess structural integrity. CN North America for the past 15 years has complemented structural analysis, strain measurement and visual inspection with acoustic emission and strain (AES) monitoring of specific fracture critical members on selected bridges. During this 15-year period AES monitoring has been used on over 215 bridges to assess the state of existing fatigue cracks, to locate hidden cracks and to assess the effectiveness of crack repairs [1-5]. It is used on a periodic basis, data on one bridge takes up to two weeks of analysis of scanning a large signal database, and on-line monitoring that is increasingly in demand is not as yet feasible. Both the data analysis and continuous monitoring can be addressed by applying intelligent systems technologies.

Data is presented for a single train pass at the location shown in Figure. 1. The bridge is located in Stevens Point, WI, and owned and operated by CN North America. The tests were undertaken from July 13 to 15, 2003 as part of an on going AES bridge monitoring contract with CN North America. The purpose of this exercise is to show the correlation between load cycles (strain data) and AE from the fatigue cracking process as typically observed on steel railway bridges. A fracture critical member with a pre-existing crack was selected for monitoring as designated by the client. The location had previously experienced fatigue cracking and had its crack tips drilled. This location was found to be currently active. WinS uses a digital strain – acoustic emission monitoring system with transient recording capabilities and an operating bandwidth from 220 – 850 kHz. 375 kHz resonant transducers, with integral preamp providing 34 dB gain are used, exclusively. All channels on the data acquisition system have a fixed (calculated) gain set to the total gain in the measurement chain. The instrument is capable of handling relatively high parametric acquisition rates without detrimental effects on AE data acquisition. All data are time stamped so exact correlation of AE hits to current parametric levels is possible. A load curve is acquired for all monitoring locations using conventional foil gauges. A parametric sampling rate of roughly 200 Hz is more than adequate for obtaining the primary loading curve. Acoustic emission data are analyzed using planar source location, load correlation, and time and frequency domain analysis. Planar location arrays are typically small (on the order of a few feet in both dimensions) due to high noise levels and constraints posed by location geometry experienced on many bridges. Figure 1 shows typical strain - acoustic emission data obtained during bridge testing. In the top left, the loading cycle is shown. During maximum loading, 908 to 910 seconds for the safety critical structure shown, crack growth is present at numerous locations in member. This curve shows the strain profiles as a train load passes over the area being monitored. On it is superimposed acoustic emission data that, on this cycle is one emission. The location of the same emission is shown on the bridge. The strain and AE data are used as a Kaiser effect validation of the emission as a deformation mechanism as opposed to noise and both strain and location correlations provide a basis for a sound evaluation of the use of strain and acoustic emission data for fitness-for service evaluation. Remote Monitoring Technology Development of the MEMS component will be a multistage objective and can proceed from the large, conventional commercial system through the compact PC104 configuration to a series of dedicated MEMS modules as shown in Error! Reference source not found.. The conventional system costs about $70,000 and the cost objective is to reduce this system cost by at least an order of magnitude. MEMS (micro-electromechanical systems) are extremely small devices utilizing both electrical and mechanical properties. They combine computing/logic circuits with tiny mechanical devices such as sensors, valves, gears, mirrors, and actuators integrated on semiconductor chips. This technology, in mass production, has the potential to achieve the targeted cost reduction. MEMS technology has powerful advantages such as low-cost, high sensitivity, durability, miniaturization of device, and suitability for the wireless system approach for large structural engineering applications involving large number of sensors. The dominant material of MEMS is silicon, which is mechanically linear, brittle and strong. Some MEMS materials are piezoresistive, and piezoelectric for sensor use. Furthermore, The MEMS-based sensors for structural applications can integrate three different functions: accelerometers, ultrasonic or acoustic transducers, and strain gauges. A CAD layout of the MEMS sensors is shown in Figure 3. Using RF communication technology eliminates the need for long cables. Thus, this concept lends itself to a wireless sensor network consisting of a number of sensors spread across a structure. Each sensor has wireless communication capability and some level of intelligence for signal processing and networking of the data.

Strain induced by loading bridge with high-capacity freight train. High strain levels can be seen in the 908 to 910 second timeframe

Repaired crack in critical member

Acoustic emission/ strain sensors.

Activity locations

AE event is correlated to passage of train showing that high load level cause an increase in crack activity and crack propagation.

The source of this acoustic emission (AE) signal is an active crack. The strain and AE data are used as a Kaiser effect validation of the emission as a deformation mechanism as opposed to noise. Both strain and location correlations provide a basis for a sound evaluation of the use of strain and acoustic emission data for fitness-for service evaluation. An active crack has significant implications on allowable loads. A dormant crack indicates that the safety critical structure can handle higher loads.

Figure 1. Crack growth detected at high bridge loading. The crack is active and safety critical member is not fit-for-service.

Figure 2. System migration path

Figure 3. CAD layout of MEMS device

Field Data from MEMS AE Sensors Field data was acquired using Carnegie Mellon’s MEMS AE sensors and compared to commercial piezoelectric AE sensor data [5-8]. The sensors were installed on a riveted connection which was a subcomponent of the floor framing system. Four AE piezoelectric sensors were coupled to the structure in addition to the CMU-MEMS device as shown in Figure 3. The CMU-MEMS device was triggered using on of the piezoelectric AE sensors. Figure 4 presents data sample data from an AE event triggered due to the freight loading of the structure. In this instance, one of the three MEMS detected the AE event.

Figure 3. CMU’s MEMS AE Sensors and TISEC’s piezoelectric AE sensors.

Figure 4. Comparison of piezoelectric AE sensor and (second trace) and three MEMS channels under freight loading showing that only one MEMS channel picks up the AE event.

The Role of Artificial Intelligence in AE Bridge Testing Since and acoustic event from an active crack typically has a characteristic waveform, various signal characteristics may be used to identify the signal as originating from an active crack and to discriminate it from noise. The general approach is shown in Figure 5.

Figure 5. Current procedure used to for evaluating AE events from active fatigue cracks. Pattern recognition, a branch of artificial intelligence, uses the statistical nature of the acoustic emission signal to identify and discriminate among signal sources [9]. It examines the statistical variation of signal features such as those shown in from the different types of emission coming from the bridge element of interest. Using unsupervised learning on the complete data set for the base metal of the laboratory specimen, a set of waveform features was extracted from each signal in the time, power, phase, cepstral and autocorrelation domains. A total of 108 features are extracted. In the time domain, 38 features are extracted comprising peak amplitudes, rise and fall times, rise slopes, distance between peaks and peak positions and widths, number of peaks above the threshold. In each of the power, phase, cepstral and autocorrelation domains, 10 features comprising peak amplitudes, distance between peaks and peak positions, number of peaks above thresholds and area under peaks are extracted along with the % partial power in each of eight power octants. Using nonlinear mapping, the complete data set for a test session is shown in Figure 1 where the 108-dimension feature space is mapped into normalized axes in a 2-dimension space to provide a graphical display of the tendency to group into patterns. To perform the clustering, a set of waveform features that provided good separation in the feature space was selected. These features were selected by visual examination of the feature histograms as shown in Figure 2 as a feature-feature plot showing the cepstral spectrum versus the power. The latter two features are two of the more effective features in this application and are particularly interesting since they reflect structural and source characteristics and are independent of coupling and many other amplitude-dependent setup parameters. The cepstrum is indicative of the power flow in time and the separation in feature space discriminates between a class of signal that is relatively continuous in time from a more discrete impulse.

Figure 1 Nonlinear mapping of waveform features using data from the base metal Acoustic emission signals observed during crack growth under cyclic loading in a representative bridge steel separate into two classes shown in Figure 3 corresponding, on the left to signals obtained on the rising and on the right, to the falling sides of the load cycle, respectively. The features used to perform the classification include, in particular, those in the cepstral domain. This is significant because this domain distinguishes between burst and continuous waveform behavior. The more burst-like emissions were observed on the rising load part of the load cycle. These two correlations, the separation of the signals into two groups with respect to load cycle and the jerky or burst-like nature on the rising load side both suggest that crack opening is occurring on the rising load side and smoother mechanisms such as crack face fretting and grinding of corrosion product are taking place on the decreasing load side. This provides a basis to separate and identify growing cracks from those that are active but not growing.

Figure 2 Feature - feature plot of the cepstral power versus the partial power

Meaningful interpretation and reporting of acoustic emission data requires a correlation to the condition of the crack(s) being monitored. Relevant AE data are presented in graphs of activity and intensity against stimulation that, in the case of bridge monitoring, is fatigue-induced train loading, a function of time. The relevant signals are used to rank the sources. The significance of relevant defects can be evaluated using both activity and intensity criteria or a combination of both, an approach influenced by the ASTM E569-02, Standard Practice for Acoustic Emission Monitoring of Structure during Controlled Stimulation. The source ranking metrics interpret acoustic emission indications in terms of their significance to the bridge engineer. The fatigue crack index relates acoustic emission signal descriptors to the condition of the crack(s) and, therefore, to structural integrity. When an emission source begins to show activity with increasing train traffic as shown on a planar location plot in Figure 4 and such clustering of activity is clearly differentiated from the distribution of indications over the complete area monitored, this area of activity is identified as a region of interest. The cumulative AE activity in terms of events or ringdown counts activity at either the nearest transducers or over the region of interest is analyzed as a function of time under load during the test. The classifications of acoustic emission activity are shown in Error! Reference source not found.. An inactive source may show some initial activity early in the time profile but turns concave downward and does not continue to emit with increasing exposure to the train loading. An active source will increase linearly with train loading and a critically active source turns concave upward and the rate of emission increases with loading.

Figure 3 Nonlinear mapping of waveform features showing separation between rising and falling load curves

Figure 4 Activity indication within the area defined by the four transducers shown on the accompanying picture.

Figure 10 Acoustic emission activity classifications.

Summary The paper summarizes the current state-of-the art in AE steel bridge testing in terms of practice and the role that artificial intelligence plays in the interpretation of AE data. A basis for separating and identifying growing cracks from those that are active but not growing is also presented. It is shown that features from the cepstral domain may be used to discriminate between burst-like and continuous AE events. By further correlating these signals to load cycle, it was shown that crack opening is occurring on the rising load side while crack face fretting and grinding of corrosion product is occurring on the decreasing load side. A migration path is also provided for development of autonomous AE bridge technology based on PC104 – MEMS technology. References 1. Irving J. Oppenheim, David W. Greve, Didem Ozevinc,f, D. Robert Hay, Thomas R. Hay,

Stephen P. Pessiki, and Nathan L. Tyson, “Structural Tests using a MEMS Acoustic Emission Sensor”, SPIE Smart Structures Conference Proceedings, 2006.

2. Nyborg, D., J. Cavaco, E. Nyborg and D.R. Hay. /Combined Application of Ultrasonic Inspection and Acoustic Emission Monitoring for Evaluation of Fracture Critical Members in Railway Bridges,/ presented at the/ /2001 Annual Conference and Exposition, American Railway Engineering and Maintenance of Way Association, Chicago, Illinois, September 9 to 12, 2001. (Published in the Conference Proceedings, available from AREMA, Landover, Maryland.)

3. Nyborg, D., J. Foster, E. Nyborg, D. R. Hay and R. W. Y. Chan. /Special Techniques for Nondestructive Evaluation of Steel Railway Bridges, /presented at the Ninth Annual Research Symposium and Spring Conference, American Society for Nondestructive Testing, Birmingham, Alabama, March 27 to 31, 2000. (Published in: /Topics on Nondestructive Evaluation TONE): Volume 5, Nondestructive Testing and Evaluation (NDT&E) for the Railroad Industry/, ASNT, Columbus, Ohio.)

4. Spanner, J.C., Sr., A. Brown, D.R. Hay, V. Mustafa, K. Notvest and A. Pollock, "Fundamentals of Acoustic Emission Testing," Nondestructive Testing Handbook, second edition: Volume 5, Acoustic Emission Testing, P. McIntire and R.K. Miller, eds., Columbus, Ohio, American Society for Nondestructive Testing, 1987, pp. 11-44.

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6. Ozevin, D., Pessiki, S.P., Jain, A., Greve, D.W., and Oppenheim, I.J., “Development of a MEMS Device for Acoustic Emission Testing,” Smart Structures and Materials 2003: Smart Systems and Nondestructive Evaluation for Civil Infrastructures, San Diego, March 2003, Proc. SPIE, Vol. 5057, pp. 64-74.

7. Ozevin, D., Greve, D.W., Oppenheim, I.J., Pessiki, S., “The Characteristics of a New Transducer Design for Acoustic Emission Testing,” Advances in Civil Engineering-6th International Conference, October 2004, Istanbul, Turkey, pp. 1014-1024.

8. Ozevin, D., Greve, D.W., Oppenheim, I.J., Pessiki, S., “Steel Plate Coupled Behavior of MEMS Transducers Developed for Acoustic Emission Testing,” 26th European Conference on Acoustic Emission Testing, September 2004, Berlin, Germany, pp. 557-564.

9. Hay, D.R., Chan, W.Y., Pattern Recognition Manual, TISEC Inc. Montreal, Canada, 2001.