Correcting Sensor Drift and Intermittency Faults With Data Fusion and Automated Learning

Goebel, Kai; Yan, Weizhong

doi:10.1109/jsyst.2008.925262

Cited by 41 publications

(20 citation statements)

References 23 publications

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“…Similarly, we have tried to boil down the diverse set of sensor processing tasks to a small set of core kernels that are generic enough to find application in many fields, and common enough to warrant the allocation of processor chip real estate. While many applications require massive processing of sensor data using digital-signalprocessing-type algorithms, there is a large base of applications [Hellerstein et al 2003;Goebel and Yan 2008;Petrov et al 2002;Suh 2007] that consist of simpler processing tasks, and do not warrant the overhead of having a full-blown Digital Signal Processor (DSP). We have identified and extracted five such tasks from the MiBench benchmark and various other sources: Linear Equations, Moving Average, Average, Delta Value, and Threshold/Range Check.…”

Section: Common Kernel For Sensor Processingmentioning

confidence: 99%

“…Averaging these distributed measurements is a simple means for aggregating this information into a compact form. This approach was used by Goebel and Yan [2008], Ganeriwal et al [2003], and Nakamura et al [2006]. In Hellerstein et al [2003] the authors use averaging as means of "cleaning" data obtained from a group of sensors.…”

Section: Common Kernel For Sensor Processingmentioning

confidence: 99%

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Hardware architectural support for control systems and sensor processing

Vyas

Gupte

Gill

et al. 2013

ACM Trans. Embed. Comput. Syst.

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The field of modern control theory and the systems used to implement these controls have shown rapid development over the last 50 years. It was often the case that those developing control algorithms could assume the computing medium was solely dedicated to the task of controlling a plant, for example, the control algorithm being implemented in software on a dedicated Digital Signal Processor (DSP), or implemented in hardware using a simple dedicated Programmable Logic Device (PLD). As time progressed, the drive to place more system functionality in a single component (reducing power, cost, and increasing reliability) has made this assumption less often true. Thus, it has been pointed out by some experts in the field of control theory (e.g., Astrom) that those developing control algorithms must take into account the effects of running their algorithms on systems that will be shared with other tasks. One aspect of the work presented in this article is a hardware architecture that allows control developers to maintain this simplifying assumption. We focus specifically on the Proportional-Integral-Derivative (PID) controller. An on-chip coprocessor has been implemented that can scale to support servicing hundreds of plants, while maintaining microsecond-level response times, tight deterministic control loop timing, and allowing the main processor to service noncontrol tasks.In order to control a plant, the controller needs information about the plant's state. Typically this information is obtained from sensors with which the plant has been instrumented. There are a number of common computations that may be performed on this sensor data before being presented to the controller (e.g., averaging and thresholding). Thus in addition to supporting PID algorithms, we have developed a Sensor Processing Unit (SPU) that off-loads these common sensor processing tasks from the main processor.We have prototyped our ideas using Field Programmable Gate Array (FPGA) technology. Through our experimental results, we show our PID execution unit gives orders of magnitude improvement in response time when servicing many plants, as compared to a standard general software implementation. We also show that the SPU scales much better than a general software implementation. In addition, these execution units allow the simplifying assumption of dedicated computing medium to hold for control algorithm development.

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Section: Common Kernel For Sensor Processingmentioning

confidence: 99%

Section: Common Kernel For Sensor Processingmentioning

confidence: 99%

Hardware architectural support for control systems and sensor processing

Vyas

Gupte

Gill

et al. 2013

ACM Trans. Embed. Comput. Syst.

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“…To solve this problem, a dynamic threshold method is proposed in [5,6] using polynomial chaos observer, yet the sensor faults are not completely isolated from other physical faults in the system. Alternatively, soft computing methods such as fuzzy methods have also been applied to generate the dynamic threshold [1,7,8,9,10]. The resulting sensor values are selected based on the confidence level of each redundant sensor and their fuzzy membership parameters during the process of data fusion and threshold generation [9].…”

Section: Introductionmentioning

confidence: 99%

Sensor fault detection by sparsity optimization

Dauwels

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Measurement faults in control systems may result in permanent damages to the system components. Therefore, sensor validation is essential before the measurements are used for any system reconfiguration. In this paper, a statistical approach for sensor fault identification is proposed. Specifically, the potential sensor fault is assumed to be an additive bias term in the measurement model. The problem of fault identification is formulated as a least-squares optimization problem with an 1 penalty on the bias term. An algorithm is further introduced to determine the regularization parameter automatically. Experimental results show that the proposed method can accurately detect multiple sensor failures from noisy measurements.

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“…amount of true positive, false positive, misclassification rate) are utilized to investigate model efficacy depending on the magnitude of introduced faults. In [2] fuzzy validation gate algorithm followed by sensor fusion is used to detect both abrupt and slowly developing or intermittent sensor malfunctions. Parameters of the model are determined using genetic algorithm and particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Wavelet-based sensor validation: Differentiating abrupt sensor faults from system dynamics

Kozionov

Kalinkin

Natekin

et al. 2011

2011 IEEE 7th International Symposium on Intelligent Signal Processing

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The problem of sensor fault detection is important part of the overall system health estimation. Without assuring correct sensor readings it is impossible to make any conclusion about system status. On the other hand, it is essential not to confuse sensor faults and abnormal sensor behavior caused by other reasons, such as dynamically changing system condition or fault in the system. In the current paper the problem of distinguishing between sensor faults and system dynamics is investigated. This problem cannot be solved by validating each sensor signal independently; it requires joint analysis of all sensors. The particular case of several redundant of wellcorrelated sensors is considered. The proposed solution consists of two steps. First, multiresolution analysis, a powerful waveletbased signal processing technique, is applied to detect signal changes both at high and low frequency scales. Then, found changes are inspected on different wavelet detail levels and decision is made whether these changes are true sensor faults or are caused by system dynamics. The proposed method is tested on data from gas turbine power plant.

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Correcting Sensor Drift and Intermittency Faults With Data Fusion and Automated Learning

Cited by 41 publications

References 23 publications

Hardware architectural support for control systems and sensor processing

Hardware architectural support for control systems and sensor processing

Sensor fault detection by sparsity optimization

Wavelet-based sensor validation: Differentiating abrupt sensor faults from system dynamics

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