Journal of Control Science and Engineering

Volume 2018, Article ID 1938490, 12 pages

https://doi.org/10.1155/2018/1938490

## A New Cooperative Anomaly Detection Method for Stacker Running Track of Automated Storage and Retrieval System in Industrial Environment

College of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China

Correspondence should be addressed to Mengting Lin; nc.ude.utjqc.sliam@210070071226

Received 29 March 2018; Revised 28 September 2018; Accepted 25 October 2018; Published 11 November 2018

Academic Editor: Daniel Morinigo-Sotelo

Copyright © 2018 Darong Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Considering the complexity and the criticality of the stacker equipment, in order to solve the problem that the stop accuracy of the stacker reduces or even fails to work due to abrasion of the running rail, this paper proposes a cooperative detection method based on Pulse Coupling Neural Network (PCNN) and wavelet transform theory to detect the abnormal points of the stacker running rail in industrial environment by analyzing the variation signals. First of all, considering the fact that the data is mixed up with noises because of the environment at the site and the possibility of the data acquisition equipment breaking down, a noise reduction method for the vibration signal data of stacker is constructed based on PCNN. Then, the basic theory of wavelet transform is introduced and then the rules of judging anomaly points on stackers’ running tracks are discussed based on wavelet transform. In addition, a cooperative detection method based on PCNN and wavelet transform theory is carried out based on the space-time distribution feature of the vibration of the stacker orbits in the industrial environment. Then the rationality of the proposed algorithm is verified by simulation through data provided by State Grid Measuring Center of China. This paper constructs a model of the abnormal point detection of the stackers in an industrial environment. The experimental simulation and example simulation show that the cooperative detection method based on PCNN and wavelet transform theory can effectively detect and locate the anomaly points of the stacker running tracks. The expansibility in engineering applications is promising. Lastly, some conclusions are discussed.

#### 1. Introduction

As is known to all, the stacker is a key equipment of automated storage and retrieval system (ASRS) in industrial environment. In practice scene of ASRS, the main function of the stacker is to grab, move, and stack goods from one shelf to another. Thus, the stability of stacker running track will influence the accuracy of grabbing and moving goods in practical engineering. As one of the key components of the stacker equipment, the track of the stacker is divided into upper and lower track. In fact, the stacking machine would wear, crack, sag, and bulge by long time running. And these defects will reduce the accuracy of the stacking machine. If the field engineer cannot detect and repair the defect as soon as possible, they may even cause the whole equipment to be worn and shut down. So, how to detect the anomaly point of stacker running rail is very important for safety performance of the whole system.

In anomaly detection, domestic and foreign researchers have made a lot of progress in all aspects. Since the 1980s, the anomaly detection problem has been widely researched in the field of statistics. For examples, [1] presented an anomaly detection algorithm based on statistical theory. Unfortunately, practice has proved that the computing results of the statistical method are not ideal, because the distribution characteristics of the data must be known in advance. Obviously, the shortage limits the application scope of the method. To solve the defect, some improved detection algorithms were put forward combining with distance in [2, 3]. The obvious characteristic of the improved algorithm is required to have a lot of domain knowledge of real systems or equipment. However, it is known to us all that the running performance of real systems or equipment is affected by various factors. Therefore, it is difficult to determine the input parameters of the algorithm. In other words, it has some disadvantages such as instability and high algorithm complexity. Thus, once the testing data is incomplete, it is difficult to determine the degree of abnormal [4]. In particular, when the data has features such as high-dimensional and sparseness, the performance of the algorithm is very poor. In order to solve the problem caused by sparse data, Rastogi and Ramaswamy proposed an improved algorithm based on density in [5]. The method can avoid the limitation of the dependence of distance to a certain context. To better handle testing data to find the abnormal data point, some detecting ideas based on depth continue to appear. For instance, some researchers presented the anomaly detection algorithm based on depth to mark each record as a point in the dimension space. In fact, the typical DEEPLOC algorithm was proposed by Struyf and Rousseeuw in [6]. One big advantage of the algorithm is that the depth value of each point may be given according to the definition of depth. The detection rule is that a record with a smaller depth is more likely to be an anomaly point than a larger one. Thus, once the data set is organized by the depth value, the algorithm only needs to carry out the outlier detection on the lower layer of the depth value. Meanwhile, the outlier detection is not required in the record on a layer with a large depth value.

Furthermore, Zhou S and Xu W have constructed the local anomaly detection algorithm based on the deviation in [7]. The algorithm and rule may solve the problem that the existing local anomaly detection algorithms do not perform data object partition. But in real running process of the systems, the high dimensional test data would lead to increased computational complexity [8]. So, the trick to find out the abnormal point from high-dimension data set is to reduce its dimensionality. Aiming at the high dimensionality of the data set, some researchers introduced the concept of local projection scoring (LPS) and proposed an efficient abnormal point detection method of high-dimensional data in [9]. Of course, some new algorithms (for instance, neural network [10] and clustering algorithm [11]) continue to be introduced and presented to detect and monitor the abnormal points of test data. The simulation results show that these models and methods are effective in real engineering and application.

In addition, some scholars had discussed the anomaly detection in the frequency domain. For instance, to overcome the disadvantages that the window size does not change with frequency, the wavelet transform theory is usually introduced to compensate for the localization defects of the short-time Fourier transform [12]. The characteristics of multiresolution (also called multiscale) can be used to observe signal gradually from coarse to fine. The detection and observational modes are favorable for detecting singularities of the signal step by step. So, the wavelet theorem is also an ideal signal time-frequency analysis and processing tools. Among them, the most representative algorithm is the time series anomaly detection method based on wavelet transform proposed in [12].

Although the engineers and scholars have made a lot of achievements in the field of the detection of anomaly spot, the identifying and detection of abnormal data is still in its infancy of research for the stacker of ASRS. The practice application results show that there are still some problems in the methods of safety supervisory of the stacker of ASRS. As is known to all, the running performance of stacker is affected by various factors such as running environment, structural characteristics, and the optimal goal of the whole system. Hence, how to implement the detection of the abnormal points is the kernel problem of safety maintenance of stacker in ASRS. At present, there are some research achievements about the detection of a stacker’s running performance. But the research is mainly focused on the structure analysis or the design of the system, and few people have done their researches from the perspective of abnormal point’s detection [13]. In recent years, many new algorithms are presented and proposed based on Internet, OPC, and the fault tree to analyze the data set acquired and measured from the running state of the stacker in [14]. In engineering application, the detecting ability of the remote fault of stacker may meet the desired purpose using the proposed algorithm presented in [15, 16]. Meanwhile, Kai Zhang and his coauthors have discussed and analyzed the monitoring method based on the multimode and multivariate statistics to monitor the running state of stacker crane in [17, 18]. Experimental results show that the anomaly detection method is effective.

However, because the running state of stacker is influenced by the state of the stacker’s component, the scene environment, the data acquisition equipment, and so on, the data set acquired from the system will contain a lot of noise. Obviously, the noise of data will reduce the accuracy of abnormal point’s judgment. Therefore, deleting and cleaning the noise from testing data is necessary to implement and accomplish the monitoring of running state of whole stacker system. To restrain the interferences of strong background noise, Huang D.R. and his coauthors have constructed a cooperated denoising algorithm for rolling bearing of stacker in [19]. The simulation results have verified the effectiveness of health monitoring of ASRS. Notice that, in the previous scenario, the actual monitoring data obtained from the real stacker is the aliasing vibration signal. So, how to accomplish the separation of multivibration signal is a difficult problem in vibration process. If the method is reasonable, the detecting accuracy of stacker’s running abnormal points will be greatly improved.

Based on the analysis and the thesis above, to ensure the effectiveness of the incomplete data processing of real system, it is necessary to construct and design a cooperative anomaly detection algorithm so that the abnormal spot can be detected and located as quickly as possible in on-site industrial environment. Notice that the timing of the real time data process is vital in the industrial environment, and then the data signal needs to be treated from coarse to fine as soon as possible. On the basis, the Pulse Coupling Neural Network (PCNN) presented in [20, 21] is introduced to denoise, because it has the advantage that the industrial data process does not depend on precise mathematics model. Meanwhile, due to the fact that the wavelet transform may complete the itemization of the data collection, it is introduced to construct and establish the abnormal point detecting algorithm to locate the stacker’s defect through the pure data processed by PCNN.

Hence, the rest of this paper will discuss the details of the algorithm and thesis. The layout of the rest of the paper is organized as follows: Section 2 will introduce the basic concepts of PCNN and the modified PCNN is introduced to construct the data denoising model. In Section 3, in order to locate the anomaly points, the wavelet transform is introduced as the anomaly detection and location algorithm. Also, the cooperative anomaly judgment algorithm and rule for anomaly detection will be discussed in detail combined with modified PCNN and wavelet transform. In addition, the algorithm flow chart is also drawn. Later, the contrast experiments and numerical simulation to detect the abnormal point of stacker running track are carried out to verify the effect of the algorithm and rule using data provided by State Grid Measuring Center of China. Finally, some conclusions and the directions for future engineering application are discussed according to the real stacker running track of ASRS in industrial environment.

#### 2. Improved PCNN Denoising Model and Algorithm for Vibration Signals of Stacker Running Track

In a real working condition, the actual vibration signals measured from this system will be unavoidably affected by many complicated environmental factors. Obviously, the data package usually includes strong noises. So, to guarantee the effectiveness of anomaly detection for stacker running track, a reasonable data preprocessing procedure is very crucial to eliminate the noises that are contained in the dataset. In this context, constructing an effective denoising model and algorithm to process the original signal is of great theoretical and practical significance for the condition monitoring of the stacker running track.

However, most industrial monitoring and control applications require high performance, timeliness, and reliability. Then, most administrators and engineers hope to effectively operate the system without knowing the accurate model. Based on this thesis, the PCNN will be introduced later. On this basis, an improved PCNN denoising model and algorithm are analyzed and designed according to the actual situation to ensure the timeliness and stability of the performance of the stacker running track.

##### 2.1. Basic Theory of PCNN

As we all know, PCNN is presented by Eckhorn based on the observed synchronous pulse transmission after the experiments of the cerebral cortex of the animals [22]. In fact, due to its scale invariance, rotation invariance, intensity invariance, distortion invariant, and other characteristics, PCNN is widely used in image smoothing, image segmentation, image edge detection, image fusion, optimal solution, and so on. Moreover, in theory, the PCNN model has similar group neurons synchronization release pulse characteristic and the accurate model is not needed to parse the structure of dataset. Thus, the engineers not only reveal the inherent ability of PCNN, but also explore the application of PCNN in the signal denoising. So, the basic concept of PCNN will be introduced in the next context.

According to [22], the PCNN model can be expressed by the following equation:where denotes the th feedback input for the neuron; , , and represent the external stimulation, internal behavior, and output of neuron , respectively; , are the two input channels for the link domain and the feedback domain of the neuron ; and are the connection weight coefficient matrix of feedback domain and link domain; and are the output and threshold amplification factor and the variable threshold function; denotes synaptic link coefficient; , , are the time attenuation constants of link domain, feedback domain, and variable threshold function, respectively.

From the perspective of simulation, the PCNN neuron consists of three parts: receiving domain, modulation domain, and pulse generation domain. In real application, PCNN has the advantage that the data processing does not depend on precise mathematics model. That is to say, in the pretreatment of denoising, once the network interface of PCNN receives the input signal, the receiving field transmits it through two channels, and . The impulse response function of channel changes slowly with time compared to channel. The modulation part combines the signal from channel with an offset and multiplies the signal from channel to generate the internal signal . Then, and will be compared to control the firing of signal neurons. If , the neurons will be activated. Otherwise, the neurons may be deleted from the structure of PCNN.

According to the link coefficient of channel and channel, PCNN can be divided into two cases: coupled and uncoupled. When , each neuron was separately operated and unaffected by the pulse output of other neurons around it. In addition, considering the friendly interface for end users, the basic structure of PCNN is shown as Figure 1.