8 Figure 1.6 The proposed framework for error detection and prediction based on pattern analysis. 168 Figure 7.2 The results of the 1st and 3rd set of abnormal cylinder temperature predictions (blue: original sensor signal, green dotted line: the corresponding label, vertical yellow dot: the time at which a symptom pattern(s) are found and vertical red: the time at which an error is predicted).
INTRODUCTION
- B ACKGROUND
- M OTIVATION
- O BJECTIVES
- O RGANIZATION OF THE THESIS
There are several fault detection and prediction methods that use univariate time series data from only one sensor. The proposed framework for error detection and prediction in pattern analysis using multivariate time series discretization is illustrated in Figure 1.6.
LITERATURE REVIEW
F AULT DETECTION AND PREDICTION USING MULTIVARIATE TIME SERIES
We categorize fault detection and forecasting methods using multivariate time series data into four categories, as illustrated in Figure 2.1. If any state information of a system (called as class information, dependent variables) is not given, then unsupervised outlier detection and prediction.
D ISTANCE BASED MODELS
- Multivariate statistical process control models
- Multivariate statistical projection models
S-trial regression was also used for multi-step forward prediction of machine system states (G. Niu & B.-S. Yang, 2009). To handle the nonlinearity in input sensor data, there are many types of PCA and PLS expansions that have already been investigated and applied for fault detection and prediction (S. J. Qin, 2012; C. Yoo, S. W. Choi, & I. -B Lee, 2008), as illustrated in Figure 2.6.
C LASSIFICATION MODELS
- Pattern extraction models
- Time segmentation
- Partitioning and labelling
- Pattern extraction
- Pattern matching
The partition means dividing the value axis into a set of coherent labels in the time series discretization according to the evaluation criteria (S. Kotsiantis & D. Kanellopoulos, 2006). On the other hand, more extensible four-character primitives were analyzed to investigate the appropriate behavior of the given time series data at once (K.B. Konstantinov & T. Yoshida, 1992). QTA was also applied in the principal components of the given sensor signals (M. R. Maurya, R. Rengaswamy, & V. Venkatasubramanian, 2005).
In addition to the SAX transformation, there are the large number of control parameters in the temporal discretization algorithms in order to obtain more clearly transformed time series data (P. Esling & C. Agon, 2012). Cheung (2014) used the dynamic length to briefly use the time segment to divide impulsive values (usually considered noises) from the given entire time series. Similarly, artificial errors were added in the given time series data, and then their algorithms were validated and verified by extracting error patterns from the artificial (J. Lin & Y. Li, 2009).
S UMMARY
It is more reasonable that the discretization was used as a function choice in relation to the error prediction. Additionally, similarity measures have been analyzed to evaluate the corresponding error probabilities (H. Al-Atat, D. Siegel, & J. Lee, 2011). There are noticeable advantages of error detection and prediction of a pattern analysis using multivariate time series data, to reveal the fundamental or hidden signal trends, to make induced information more concise, to reduce high dimensionality of the given data sets, to use cheap computational cost, and to be robust to noise (I. Alles, 2013; P. Esling & C. Agon, 2012; S. García et al., 2013).
Therefore, this study proposes a framework for fault detection and prediction that consists of (i) definition of the operational states of the system, (ii) definitions of fault and symptom patterns, (iii) multivariate discretization, (iv) analyzes of severity and criticality, and (v ) online detection and prediction procedures.
OPERATIONAL STATES
F AULT , NO - FAULT , NORMAL , AND SYMPTOM STATES
C ONDITION MONITORING AND MULTIPLE SENSOR SIGNALS
- Abnormal cylinder temperature in a marine diesel engine
- Automotive gasoline engine knockings
- Laser weld defects
- Buzz, squeak, and rattle (BSR) noises from a car door trim I (using a typical acoustic
- BSR noises from a car door trim II (using acoustic emission sensors)
- Visual stimuli cognition tests by theP300 experiment
As a result, there is a relatively large type I error and type II error (45.3% and 20.0% in Figure 3.3). Q-statistics) were calculated as the final result in PCA-based error detection. The system shown in Figure 3.7 has been developed to detect a defective car door trim during a Figure 3.6 Normal and defective weld seams with the associated weld pool temperature signals. Despite the ambient noise, the acoustic emission signals exhibit clearer behavior than the conventional sound signals, as shown in Figure 3.10.
During the presentation of the stimuli, a participant is supposed to focus on the given target stimuli. flashing time: approx. 120ms per stimulus).
MULTIVARIATE TIME SEIRES DISCRETIZATION
- O VERVIEW
- L ABEL DEFINITION
- Step A1: Estimation of the distribution models for sensor signals
- Step A2: Cut-point determination
- Step A3: Consideration of the linear trend in the time segment
- Step A4: Generation of a set of labels
- L ABEL SPECIFICATION
- Step B1: Time segmentation
- Step B2: Labelling
- E VENT CODIFICATION
For further data reduction in the time axis, time series discretization can be done based on time segment information of sensor data. To further account for the linear trend of sensor data, we refer to the slope of the regression line in a time segment. From the predefined set of labels 𝐿𝑖, we need to select an appropriate label for each time segment with respect to the mean value of the sensor data and the linear trend in the time segment as described in Algorithm 4.1 (see from (c) to (d) in Figure 4.3).
In the case of a set of adjacent DSVs, we can obtain them by shifting a window of a predetermined number of time segments of the discretized multivariate time series data.
FAULT PATTERN EXTRACTION
D EFINITION OF FAULT PATTERN
According to Definition 1, the event code '213' and '312' are finally derived as a set of error patterns because they are only found in the two given error states, but not in the given no-error states, in Figure 5.2. . Note that the event code '213' occurred in all error conditions, and thus can be considered as a stronger evidence of the occurrence of the defect. Let us consider a fault condition to be 'detectable' if at least one fault pattern is found in the state, since the fault pattern is a unique signal change that is detected only in fault states, as explained by Definition 1.
Finally, in order to extract the set of fault patterns to be found in the fault states, not Figure 5.2 Example of fault pattern extraction (where m = 3, s = 20 and only considering the set of individual DSVs as event codes). .
T HE SEVERITY OF A FAULT PATTERN AND THE CRITICALITY OF A FAULT STATE
- The severity degree of a fault pattern
- The criticality of a fault state
This means that the severity level of the defect pattern is estimated in proportion to the number of the defect pattern identified and will consequently be used to quantitatively assess the effect of the defect pattern. Since the severity level is calculated based on the number of corresponding occurrences of the pattern in each error state, a higher value indicates that the error pattern is found in many error states and can therefore be considered significant. After counting the number of occurrences of a fault pattern, the number of occurrences is divided by the total number of a given fault condition as a normalization step.
For example, Figure 5.2-(b) shows the computational procedure for calculating the severity rating of the extracted fault patterns.
E MPIRICAL SENSITIVITY ANALYSIS FOR MULTIVARIATE DISCRETIZATION PARAMETERS
- Experimental design
- Key characteristic indicators of sensor signals
- Computational results
- Discretization parameter selection
The six levels of the time slice are designed with respect to the average length of fault states. In the case of discretization problem (ii), which consists of extracting a set of total error patterns found in the error states, the main effects of time segment length, number of bins and linear T are significant, as shown in Table 5.2 . In other words, controlling the duration has no significant effect on the performance of the error pattern extraction.
In particular, for the case of discretization problem (i), the result of computational experiments shows that when the ratio of the length of time segment to the average length of fault conditions is not greater than 10%, a good performance of fault pattern extraction is expected, as summarized in Table 5.10.
D ISCUSSION
- Comparison of the proposed fault pattern extraction with the conventional detection
- Multi-sensor signal selection for fault pattern extraction
- Performance improvement plan through conventional machine learning models
To determine whether multi-sensor signals provide better detection results than a single signal, we compare the error pattern extraction performance between multiple sensors and a single sensor. Therefore, we can conclude that multi-sensor signals are suitable for extracting significant patterns by considering not only the behavior of a single signal, but also the combinations of the behavior of multiple signals. ANOVA can also be used to determine the statistical significance between the set of applied sensor signals and the performance of error pattern extraction.
In addition, the research performs threshold value determination for each sensor signal separately, but it is possible to divide multivariate sensor signals into.
SYMPTOM PATTERN EXTRACTION
S YMPTOM PATTERN AND ITS SEVERITY
We therefore extend the previous symptom pattern definition with Definition 8 and Algorithm 6.2 explains a modified extraction procedure for new symptom patterns. Let's assume that a symptom condition is "observable" if at least one symptom pattern is found in the condition, so that the related fault can be alerted before it occurs. If a particular symptom pattern is found in multiple symptom states, the pattern may be treated as severe.
Therefore, we define the severity of a symptom pattern by the ratio of the number of symptom states in which the particular pattern is found to the total number of symptom states.
S YMPTOM STATE LENGTH DETERMINATION
This study discusses how to define the search space as a symptom state by iteratively searching symptom patterns as described in Algorithm 6.4. The search continues backward in time by sliding the time window by a predetermined length (e.g., three times Figure 6.3 Detected low-reverse electromagnetic sensor signal that clearly determines the condition of the symptom before the engine knocks. If no new symptom pattern is found or the overall severity level is lower from a predetermined threshold in the current time window (eg from time segment 14 to 16 in Figure 6.4), we shift the time window by a.
For example, in Figure 6.4 we can consider the symptom state from time segment 17 to 25, for the error that occurs from time segment 26 to 30.
ONLINE FAULT DETECTION AND PREDICTION
- D ETECTION AND PREDICTION PROCEDURES
- A BNORMAL CYLINDER TEMPERATURE PREDICTION
- G ASOLINE ENGINE KNOCKING PREDICTION
- BSR NOISE DETECTION
Therefore, in this case, we calculate the severity of the extracted pattern and the criticality of the current condition. For the determination of the symptom state length, the decay time was set to 20% of the average length of no-error states. However, in the case of the 51st of normal and symptom case, it showed a gradual increase in the total cumulative severity at about 110 seconds.
An example of online fault detection is shown in Figure 7.5, the fault is successfully detected and recognized when the BSR noise is generated.
CONCLUSION
Fault detection using the fault pattern): to extract a set of fault patterns that are found
Discretization problem for fault detection): to find out a set of discretization
DI): Discernibility index of a multivariate time series
Symptom pattern): a set of event codes (which are a set of individual DSVs or a set of
Symptom pattern+): a set of event codes (which are a set of individual DSVs or a set
Generation of a set of labels - partitioning
Assign a relevant label to a time segment
Fault pattern extraction
Discernible fault states
A severity degree of a fault pattern
Symptom pattern extraction according to Definition 7
Symptom pattern extraction according to Definition 8
A severity degree of a symptom pattern
Determination of a symptom state length
