Leak detection in gas pipeline networks using an efficient state estimator. Part II. Experimental and field evaluation

In Part-I of this two part paper, a method is proposed for on-line leak detection and identification in gas pipeline networks using flow and pressure measurements. Simulations on two illustrative networks were used to demonstrate the applicability of the proposed method. In this paper, the performance of the proposed leak detection and identification methodology was evaluated using experiments with compressed air on a laboratory scale network. The on-line applicability of the proposed methodology was demonstrated through field level leak detection tests carried out on a 204.7 km long pipeline in India, supplying natural gas to a power plant. The laboratory and field tests demonstrated that the proposed methodology can be used for quick on-line detection of leaks, and locating the leaks reasonably accurately.     

Sequential Anomaly Detection in Wireless Sensor Networks and Effects of Long-Range Dependent Data

Abstract

Anomaly detection is important for the correct functioning of wireless sensor networks. Recent studies have shown that node mobility along with spatial correlation of the monitored phenomenon in sensor networks can lead to observation data that have long range dependency, which could significantly increase the difficulty of anomaly detection. In this article, we develop an anomaly detection scheme based on multiscale analysis of the long-range dependent traffic to address this challenge. In this proposed detection scheme, the discrete wavelet transform is used to approximately de-correlate the traffic data and capture data characteristics in different timescales. The remaining dependencies are then captured by a multilevel hidden Markov model in the wavelet domain. To estimate the model parameters, we develop an online discounting expectation maximization (EM) algorithm, which also tracks variations of the estimated models over time. Network anomalies are detected as abrupt changes in the tracked model variation scores. Statistical properties of our detection scheme are evaluated numerically using long-range dependent time series. We also evaluate our detection scheme in malicious scenarios simulated using the NS-2 network simulator.     

Sequential adaptive networks: An ensemble of neural networks for feed forward control of L-methionine production

Intracellular methionine synthesis is strictly regulated and apparently, results in highly nonlinear concentration-time profiles observed in fed-batch production of this amino acid. For controlling methionine concentration along a predefined trajectory, a control strategy was developed using a sequential adaptive network (SAN) in conjunction with a mechanistic feedforward control law. SAN is an assembly of chronologically ordered networks, with one sub network assigned to each sampling interval, so that feature memory is distributed. Data for training SAN was obtained using a model whose parameters were calculated from experimental data. A range of different operating regimes was simulated using the model to create process scenarios for evaluating the performance of the SAN-feedforward controller (SAN-FFC). The adaptation of the weights of SAN is driven by the error between predicted and measured values of dissolved oxygen concentration at each sampling interval. Under simulated conditions, the feedforward control law uses the values of state variables predicted by SAN and measured values to determine a control action that is in tune with process evolution. The SAN-feedforward controller is robust and exhibits stable tracking of the methionine concentration trajectory in the presence of measurement noise and parametric uncertainty. It is perceived that the online implementation of SAN-FFC for a general bioprocess is practicable.     

Electrochemical diamond sensors for TNT detection in water

An electrochemically stabilized boron doped diamond electrode prepared by chemical vapour deposition (CVD) is used for electrochemical TNT sensing in aqueous solutions. Square wave voltammograms (SWVs) exhibit three highly resolved peaks at −0.47, −0.62 and −0.76 V vs. Ag–AgCl reference electrode, respectively. The current vs. TNT concentration plot shows a linear relationship with a same slope for the two first TNT peaks at μg L⁻¹ and mg L⁻¹ concentration ranges. Detection and quantification limits of 10 and 25 μg L⁻¹, respectively, were obtained without any preconcentration step. Relative standard deviation (RSD) of less than 1% measured over 10 runs has been found for the −0.47 V peak current showing the very high stability of the electrode without any significant fouling effect. An interference study with nitro aromatic compounds of the same family (nitro toluene and dinitrotoluene) has shown that the −0.47 V reduction peak enables TNT discrimination. Measurement of TNT in a natural medium (sea water without any purification step except filtering) has been also investigated.     

Multiway continuous hidden Markov model‐based approach for fault detection and diagnosis

A fault detection and classification scheme that uses probabilistic inference based on multiway continuous hidden Markov models (MCHMM) which is capable of capturing complex system dynamics and uncertainty is proposed. A set of observations from normal and faulty runs of the system was collected and used to generate the training dataset. The training data is assumed to follow a finite Gaussian mixture model. The number of mixture components and associated parameters for the optimal Gaussian mixture fit of the observed data was computed subsequently by clustering using the Figueiredo–Jain algorithm for unsupervised learning. The segmental k‐means algorithm was used to compute the HMM parameters. The applicability of the proposed scheme is investigated for the case of an inverted pendulum system and a fluidized catalytic cracker. The monitoring results for the above cases with the proposed scheme was found to be superior to the multiway discrete hidden Markov model (MDHMM) based scheme in terms of the accuracy of fault detection, especially in case of noisy observations. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2035–2047, 2014     

Multiway discrete hidden Markov model‐based approach for dynamic batch process monitoring and fault classification

A new multiway discrete hidden Markov model (MDHMM)‐based approach is proposed in this article for fault detection and classification in complex batch or semibatch process with inherent dynamics and system uncertainty. The probabilistic inference along the state transitions in MDHMM can effectively extract the dynamic and stochastic patterns in the process operation. Furthermore, the used multiway analysis is able to transform the three‐dimensional (3‐D) data matrices into 2‐D measurement‐state data sets for hidden Markov model estimation and state path optimization. The proposed MDHMM approach is applied to fed‐batch penicillin fermentation process and compared to the conventional multiway principal component analysis (MPCA) and multiway dynamic principal component analysis (MDPCA) methods in three faulty scenarios. The monitoring results demonstrate that the MDHMM approach is superior to both the MPCA and MDPCA methods in terms of fault detection and false alarm rates. In addition, the supervised MDHMM approach is able to classify different types of process faults with high fidelity. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Power of the MOSUM test for online detection of a transient change in mean

Abstract

In this article we discuss an online moving sum (MOSUM) test for detection of a transient change in the mean of a sequence of independent and identically distributed (i.i.d.) normal random variables. By using a well-developed theory for continuous time Gaussian processes and subsequently correcting the results for discrete time, we provide accurate approximations for the average run length (ARL) and power of the test. We check theoretical results against simulations, compare the power of the MOSUM test with that of the cumulative sum (CUSUM), and briefly consider the cases of nonnormal random variables and weighted sums.