FEM4- Analysis and Interpretation of Sensor Data

1. FEM4- Analysis and Interpretation of Sensor Data Fundamental Education Subunit- Structural Health Monitoring Education Unit

2. Purpose • To expand your knowledge base on the topic of SHM data analysis. • To develop your knowledge of technical terms related to data handling while understanding the steps that acquired SHM data go through to assess structural behavior and/or damage. Fundamental Education Subunit- Structural Health Monitoring Education Unit

3. Expected Learning Outcomes • At the completion of this module you will be able to: • explain the various possible sources of errors in SHM and the means used to minimize such errors. • identify factors/conditions that affect SHM sensor performance/data and explain methods to minimize the effects of these factors/conditions. • explain methods used to interpret and analyze SHM sensor data for the purpose of identifying anomalies. Fundamental Education Subunit- Structural Health Monitoring Education Unit

4. Your Assignment • After you have read and reviewed the content provided, you will be required to: • Take an online readiness exam to determine if you have achieved a satisfactory understanding of the content of FEM4 to engage in a discussion. • Submit a response to a discussion question related to the content of this module and list any questions you have concerning anything you might not understand about the material. • Student responses to the discussion question will be discussed in an interactive manner in a classroom setting on the date indicated on the Master Schedule. Fundamental Education Subunit- Structural Health Monitoring Education Unit

5. The Statistical Pattern Recognition Paradigm (1) • One approach to the selection, analysis, and interpretation of SHM data is termed the “statistical pattern recognition paradigm” (Farrar and Worden, 2007). • This approach encompasses the following four steps each of which is explained on the indicated slides: • A. Operational evaluation (Slides 7-9) • B. Data acquisition, normalization, and cleansing (Slides 10-29) • C. Feature selection and information condensation (Slides 30-40) • D. Statistical development for future discrimination (Slides 41-50) Fundamental Education Subunit- Structural Health Monitoring Education Unit

6. The Statistical Pattern Recognition Paradigm (2) Data Mining Diagram Fundamental Education Subunit- Structural Health Monitoring Education Unit

7. A. Operational Evaluation (1) • As presented by Farrar and Worden (2007), this phase of the SHM process consists of answering the following four questions: • What are the life safety and/or economic justifications for performing SHM? • How is damage defined for the system being investigated and, for multiple damage possibilities, which cases are of the most concern? • What are the conditions, operational and environmental, under which the system to be monitored functions? • What are the limitations on acquiring data in the operational environment? Fundamental Education Subunit- Structural Health Monitoring Education Unit

8. A. Operational Evaluation (2) Operational Evaluation Steps Fundamental Education Subunit- Structural Health Monitoring Education Unit

9. A. Operational Evaluation (3) • Operational evaluation begins to set the limitations on what will be monitored and how the monitoring will be accomplished. • This evaluation starts to tailor the damage identification process to features that are unique to the system being monitored and tries to take advantage of unique features of the damage that is to be detected. • Whereas the specific approach herein discussed is targeted to assess damage, not all SHM installations are implemented for that purpose (See Slides 23-27 of FEM1). Fundamental Education Subunit- Structural Health Monitoring Education Unit

10. B. Data Acquisition, Normalization and Cleansing • FEM3 was devoted to sensor and data acquisition technology and included a brief discussion of data normalization and cleansing (see Slides 67- 71). • Since good quality, reliable data are fundamental to the SHM process, additional discussion of SHM data processing is warranted. Fundamental Education Subunit- Structural Health Monitoring Education Unit

11. B. Data Acquisition, Normalization and Cleansing: Signal Conditioning (1) “To achieve best-in-class quality for measurements, you need to consider the several types of conditioning required for sensor measurements as well as the several types of analog components used in the instrumentation including analog-to-digital converters (ADCs)” (http://www.ni.com/white-paper/9078/en/ ). Fundamental Education Subunit- Structural Health Monitoring Education Unit

12. B. Data Acquisition, Normalization and Cleansing: Signal Conditioning (2) Signal Conditioning Diagram Fundamental Education Subunit- Structural Health Monitoring Education Unit

13. B. Data Acquisition, Normalization and Cleansing: Signal Conditioning (3) • An Analog to Digital Converter (ADC) takes an analog signal and turns it into a binary number. • Each binary number from the ADC represents a certain voltage level, and the ADC returns the highest possible level without going over the actual voltage level of the analog signal. • Resolution refers to the number of binary levels the ADC can use to represent a signal. The higher the number of binary levels, the higher the resolution and therefore accuracy. Fundamental Education Subunit- Structural Health Monitoring Education Unit

14. B. Data Acquisition, Normalization and Cleansing: Signal Conditioning (4) • The figure on the following slide shows a digital representation of signals by 12-, 16-, and 24-bit ADCs. • We currently have access to 24-bit technology, which allows for extremely accurate measurements, for static as well as dynamic applications. • Resolution refers to the number of binary levels the ADC can use to represent a signal. Fundamental Education Subunit- Structural Health Monitoring Education Unit

15. B. Data Acquisition, Normalization and Cleansing: Signal Conditioning (5) Digital Representation of Signals Fundamental Education Subunit- Structural Health Monitoring Education Unit

16. B. Data Acquisition, Normalization and Cleansing: Data Normalization (1) • Data normalization is the process whereby changes in sensor readings caused by damage are separated from those caused by operational and environmental conditions. • The term normalization refers to a procedure whereby the sensor outputs (measured responses) are referenced to the measured inputs (e.g., variations in load, temperature, wind direction and velocity). • A knowledge and use of statistical methods are important to the data normalization process. Fundamental Education Subunit- Structural Health Monitoring Education Unit

17. B. Data Acquisition, Normalization and Cleansing: Data Normalization (2) Un-normalized vs. Normalized Data6 Fundamental Education Subunit- Structural Health Monitoring Education Unit

18. B. Data Acquisition, Normalization and Cleansing: Data Normalization (3) • In circumstances of significant environment or operational variability, it may be necessary to normalize the data within a defined time frame to allow comparison of the data at similar times of an environmental or operational cycle. • During the early stages of data acquisition, data variability and its sources need to be identified and monitored. • Efforts should be made to either eliminate or minimize the factors contributing to data variability. It’s unlikely that variability can be completely eliminated. The goal is to achieve low variability as depicted on the following slide. Fundamental Education Subunit- Structural Health Monitoring Education Unit

19. B. Data Acquisition, Normalization and Cleansing: Data Normalization (4) Examples of Data Variability7 Fundamental Education Subunit- Structural Health Monitoring Education Unit

20. B. Data Acquisition, Normalization and Cleansing: Data Normalization (5) • Any analysis of data variability must be conducted under reasonably consistent environmental and operational conditions. • Thus, the sensor network must include an adequate array of sensors to measure environmental (temperature; wind speed, direction and velocity; and, possibly, rainfall/snowfall) and operational (load) conditions. Fundamental Education Subunit- Structural Health Monitoring Education Unit

21. B. Data Acquisition, Normalization and Cleansing: Data Cleansing (1) • Most data acquisition systems allow the user to selectively choose to pass on or reject data. • This process is generally under the control of an experienced individual who is directly involved with the SHM project, particularly the data acquisition component. Fundamental Education Subunit- Structural Health Monitoring Education Unit

22. B. Data Acquisition, Normalization and Cleansing: Data Cleansing (2) • Data deduced from obviously unreliable and/or inaccurate signals should be selectively deleted from the data set. • Care should be taken to ensure that elimination of readings from a given sensor is technically justified. Fundamental Education Subunit- Structural Health Monitoring Education Unit

23. B. Data Acquisition, Normalization and Cleansing: Data Cleansing (3) • Whereas the data cleansing process generally takes place in the early stages of implementation of a SHM project, it along with other aspects of data acquisition and processing should not be static. • That is, the data should be continually monitored to ensure its reliability and accuracy. • Data anomalies should be investigated and resolved immediately. Fundamental Education Subunit- Structural Health Monitoring Education Unit

24. Data Acquisition, Normalization and Cleansing: Data Cleansing (4) Data Cleansing Diagrams8 Fundamental Education Subunit- Structural Health Monitoring Education Unit

25. B. Data Acquisition, Normalization and Cleansing: Data Cleansing (5) • Signal processing techniques such as filtering and resampling can also be thought of as data cleansing procedures. • Thus, the SHM design and implementation team should include professionals with skills and experience in signal processing. This emphasizes the need for a multi-disciplinary team of engineers and technicians for SHM projects. • The results of a data cleansing process are graphically depicted in the following slide. Fundamental Education Subunit- Structural Health Monitoring Education Unit

26. B. Data Acquisition, Normalization and Cleansing: Data Cleansing (6) Example of Data Resampling9 Fundamental Education Subunit- Structural Health Monitoring Education Unit

27. B. Data Acquisition, Normalization and Cleansing: Data Cleansing (7) • Alternatively, data cleansing could be performed during the post-processing stage. In this case, almost all raw data will be acquired and stored with minimal exclusions during the data acquisition stage. • Different algorithms can then be applied to the stored data to get rid of anomalies or erroneous readings. • Again, an experienced engineer with knowledge in both structural engineering and SHM should perform this task. Fundamental Education Subunit- Structural Health Monitoring Education Unit

28. B. Data Acquisition, Normalization and Cleansing: Data Cleansing (8) • The idea is not to miss any data that may be pointing to actual structural behavior. • For example, crack initiation may lead to a sudden increase in sensor reading if it passes through the gage length of the sensor, or a sudden drop in the reading if it is just outside the sensor gage length. • Such changes are actual structural behavior and should not be taken out during the data cleansing stage. Fundamental Education Subunit- Structural Health Monitoring Education Unit

29. B. Data Acquisition, Normalization and Cleansing: Data Cleansing (9) Example of Crack Initiation: A structural behavior that should not be taken out during the Data Cleansing Cycle. 10 Fundamental Education Subunit- Structural Health Monitoring Education Unit

30. C. Feature Selection and Information Condensation (1) • This stage of the SHM data analysis process is concerned with the identification of data features that allows one to distinguish between the undamaged and damaged structure. • In many cases, this requires establishing a ‘baseline’ for subsequent comparisons in order to distinguish between damaged and non-damaged conditions. Fundamental Education Subunit- Structural Health Monitoring Education Unit

31. C. Feature Selection and Information Condensation (2) Example of Feature Selection and Information Condensation11 Fundamental Education Subunit- Structural Health Monitoring Education Unit

32. C. Feature Selection and Information Condensation (3) • The response of the structure prior to damage occurrence is used as the baseline. • Inherent in this feature selection process is the condensation of the data. • The best features for damage identification are, again, application specific. Fundamental Education Subunit- Structural Health Monitoring Education Unit

33. C. Feature Selection and Information Condensation (4) • One of the most common feature extraction methods is based on correlating measured system response quantities, such as vibration amplitude or frequency, with the first-hand observations of the degrading system. • Another method of developing features for damage identification is to apply engineered flaws, similar to ones expected in actual operating conditions, to systems and develop an initial understanding of the parameters that are sensitive to the expected damage. Fundamental Education Subunit- Structural Health Monitoring Education Unit

34. C. Feature Selection and Information Condensation (5) • The flawed system can also be used to validate that the diagnostic measurements are sensitive enough to distinguish between features identified from the undamaged and damaged system. • The use of analytical tools such as experimentally validated finite element models can be a great asset in this process. • In many cases, the analytical tools are used to perform numerical experiments where the flaws are introduced through computer simulation. Fundamental Education Subunit- Structural Health Monitoring Education Unit

35. C. Feature Selection and Information Condensation (6) • Damage accumulation testing, during which significant structural components of the system under study are degraded by subjecting them to realistic loading conditions, can also be used to identify appropriate features. • This process may involve induced-damage testing, fatigue testing, corrosion growth or temperature cycling to accumulate certain types of damage in an accelerated fashion. Fundamental Education Subunit- Structural Health Monitoring Education Unit

36. C. Feature Selection and Information Condensation (7) Example of various data accumulation testing.13 Fundamental Education Subunit- Structural Health Monitoring Education Unit

37. C. Feature Selection and Information Condensation (8) • Insight into the appropriate features can be gained from several types of analytical and experimental studies as previously described and is usually the result of information obtained from some combination of these studies. Fundamental Education Subunit- Structural Health Monitoring Education Unit

38. C. Feature Selection and Information Condensation (9) • The operational implementation and diagnostic measurement technologies needed to perform SHM produce an excessive amount of data which complicates the analysis. • Farrar and Worden (2007) suggested the following analysis strategy to help deal with this circumstance: “A condensation of the data is advantageous and necessary when comparisons of many feature sets obtained over the lifetime of the structure are envisioned.” Fundamental Education Subunit- Structural Health Monitoring Education Unit

39. C. Feature Selection and Information Condensation (10) • Because data will be acquired from a structure over an extended period of time and in an operational environment, robust data reduction techniques must be developed to retain feature sensitivity to the structural behavior changes of interest in the presence of environmental and operational variability. • To further aid in the extraction and recording of quality data needed to perform SHM, the statistical significance of the features should be characterized and used in the condensation process. Fundamental Education Subunit- Structural Health Monitoring Education Unit

40. C. Feature Selection and Information Condensation (11) Diagram of Data Reduction Techniques Fundamental Education Subunit- Structural Health Monitoring Education Unit

41. D. Statistical Development for Future Discrimination (1) • According to Farrar and Worden (2007): “Statistical model development is concerned with the implementation of the algorithms that operate on the extracted features to quantify the damage state of the structure. • The algorithms used in statistical model development usually fall into three categories: supervised learning, unsupervised learning, and semi-supervised learning. Fundamental Education Subunit- Structural Health Monitoring Education Unit

42. D. Statistical Development for Future Discrimination (2): Category 1- Supervised Learning • When data are available from both the undamaged and damaged structure, the statistical pattern recognition algorithms fall into the general classification referred to as supervised learning.  • Group classification and regression analysis are categories of the supervised learning algorithms.  Fundamental Education Subunit- Structural Health Monitoring Education Unit

43. D. Statistical Development for Future Discrimination (3): Category 2- Unsupervised Learning • Unsupervised learning refers to algorithms that are applied to data not containing examples from the damaged structure.  • Outlier or novelty detection is the primary class of algorithms applied in unsupervised learning applications. All of the algorithms analyze statistical distributions of the measured or derived features to enhance the damage identification process.” Fundamental Education Subunit- Structural Health Monitoring Education Unit

44. D. Statistical Development for Future Discrimination (4) Examples of Unsupervised and Supervised Learning15 Fundamental Education Subunit- Structural Health Monitoring Education Unit

45. D. Statistical Development for Future Discrimination (5) • “The damage state of a system can be described as a five-step process along the lines of the process discussed in Rytter (1993) to answer the following questions. • Existence. Is there damage in the system? • Location. Where is the damage in the system? • Type. What kind of damage is present? • Extent. How severe is the damage? • Prognosis. How much useful life remains? • Answers to these questions in the order presented represent increasing knowledge of the damage state. Fundamental Education Subunit- Structural Health Monitoring Education Unit

46. D. Statistical Development for Future Discrimination (6) • When applied in an unsupervised learning mode, statistical models are typically used to answer questions regarding the existence and location of damage. • When applied in a supervised learning mode and coupled with analytical models, the statistical procedures can be used to better determine the type of damage, the extent of damage and remaining useful life of the structure. Fundamental Education Subunit- Structural Health Monitoring Education Unit

47. D. Statistical Development for Future Discrimination (7) • The statistical models are also used to minimize false indications of damage. • False indications of damage fall into two categories: • false-positive damage indication (indication of damage when none is present) • false-negative damage indication (no indication of damage when damage is present) Fundamental Education Subunit- Structural Health Monitoring Education Unit

48. D. Statistical Development for Future Discrimination (8) Example of False Positives and False Negatives16 Fundamental Education Subunit- Structural Health Monitoring Education Unit

49. D. Statistical Development for Future Discrimination (9) • False positive errors are undesirable, as they will cause unnecessary downtime and consequent loss of revenue as well as loss of confidence in the monitoring system. • More importantly, there are clear safety issues if misclassifications of false negative data occur. Fundamental Education Subunit- Structural Health Monitoring Education Unit

50. D. Statistical Development for Future Discrimination (10) • Many pattern recognition algorithms allow one to weigh one type of error above the other; this weighting may be one of the factors decided at the operational evaluation stage. • Articles appearing within this theme issue that focus on the statistical modelling portion of the SHM process include: Hayton et al. (2007), Sohn (2007) and Worden & Manson (2007). Fundamental Education Subunit- Structural Health Monitoring Education Unit