*1.1. Introduction*

In today's globally complex and competitive business environments, quality is one of the crucial issues for ensuring the success of enterprises [1]. In order to produce with the desired quality and meet the customer's expectations, production processes need to be monitored to avoid any defect and deviation [2]. Traditionally, statistical process control (SPC) was used as a powerful approach for monitoring and identifying variations manually [1,2]. Developments in manufacturing and information technology enabled SPC to move from merely statistical control to real-time diagnosis purposes with minimum human intervention [3]. Control charts, invented by Shewhart in the 1920s, are essential tools in SPC to assist in controlling the behavior of the process. These tools are used to decide if the process is behaving as intended or in the presence of some unnatural causes of variations. X-bar and R charts are basic Shewhart control charts for drawing a series of process measured data with control limits [3,4]. Process variation emerges from either common causes (natural variations) or specific causes (assignable reasons). Specific causes are those that cause changes and short-term fluctuations, and, if they occur, they destroy the stability of the process, which ought to be known and eliminated as quickly as time permits. Common causes are because of the inherent characteristics of the process, and, if they exist, deviations (background noise) are in control [5,6]. However, the most crucial ability of control charts is detecting various types of patterns consisting of a series of consecutive points that are observed on these charts, which reflects fluctuations in the process [7]. The control chart patterns (CCPs) are generally divided into natural and unnatural patterns. Natural patterns usually exist in the manufacturing process and indicate that the process is statistically stable. As long as the measured data are inside the control limits or only natural random patterns exist, the process is under control. When some measurements fall out of the control limits or the measured data within the control limits signify a non-random pattern, the process is deemed out of control. Unnatural patterns displayed in control charts can be of various types, and each class can be related to specific causes unfavorably influencing the process stability. For example, "*Shift*" patterns may be related to variations in raw material, supplier, or machine, whereas "*Trend*" patterns may occur due to gauge wear or environmental changes [1,8]. Di fferent common patterns that regularly emerge in control charts can be found in Figure 1. Over time, various further decision rules such as "*zone tests*" and "*run rules*", including "*Western Electric,*" "*Nelson*", etc., were developed to assist quality control engineers and operators in detecting unnatural CCPs and circumstances leading to a change in the process [9]. Table 1 shows the most recommended rules for the Shewhart control charts to identify abnormal patterns and interpret their characteristic signs in the control chart. In general, the use of run rules can result in quickly signaling a shift in the process. However, the application of all these rules, when no particular cause exists, increases the risk of false alarms (Type I errors) to an unacceptable extent. In addition, run rules do not provide valuable pattern-related information because of a lack of su fficient pattern discrimination capability. Furthermore, control charts do not consider prior knowledge or adequate historical data. Therefore, these decision rules are not particularly useful for CCP recognition [10,11]. Since the analysis of control charts is complicated, because it relies on considerable statistical knowledge, skill, and experience of the practitioners (quality control personnel), developing an e fficient automated pattern recognition system that can ensure steady and unbiased analysis of CCPs can compensate for this gap [12].

**Figure 1.** Typical patterns in control charts.



#### *1.2. Background and Problem Statement*

With the development in manufacturing and computing technology, several approaches were proposed using artificial intelligence technologies such as artificial neural networks (ANNs), expert systems (ESs), and fuzzy sets to automatically and intelligently CCP recognition [13]. In the domain of SPC, fast and accurate control, as well as observing the variation of quality characteristics and, consequently, recognition of unnatural patterns, is the primary purpose of each fault detection and diagnosis system. There are numerous studies in this field on CCP recognition that used di fferent machine learning algorithms and other intelligent approaches, namely, K-nearest neighbors (KNN), decision trees (DT), NN-based models, ES-based models, support vector machine (SVM), wavelet-based models, and fuzzy logic [14–16]. These approaches aim at extracting meaningful information from a large amount of data to detect instabilities in the process with minimal time and cost and maximum accuracy [17]. To sum up, the most significant approaches are explained briefly in Table 2 by highlighting their advantages and disadvantages.


#### **Table 2.** Related works.

The literature review shows that ANNs and ESs are the most widely used approaches, being easier to understand and implement and having higher performance in comparison to other CCP recognition approaches mentioned above. NNs are suitable for SPC as they are good at classification and pattern recognition, and they are able to handle the noisy measurements with no requirement for the provision of explicit rules regarding the monitored data [20]. Notably, ESs are useful for quality control applications due to their potential for identifying causes of deviations and recommending preventive and corrective actions [23]. There are two approaches to applying ANNs to CCP recognition: (1) using neural networks (NNs) to detect variation in X-bar and/or R charts, and (2) using NNs to identify unnatural patterns [19]. In this regard, NNs can be classified into two main categories: supervised NNs, involving multilayer perceptron (MLP) and radial basis function (RBF), and unsupervised NNs, including learning vector quantization (LVQ) and adaptive resonance theory (ART) [10]. Among the ANNs, the multilayer perceptron (MLP) was successfully exploited by many researchers in order to address the unnatural CCP recognition problem. Learning vector quantization (LVQ) is a well-applied alternative method to solve the problem of slowness in training the MLP network [4,12,20,27].

The fault diagnosis is an essential issue in SPC, to reduce downtime and disruption cascades that can ensue [24]. In recent years, various diagnostic systems were developed to automate fault diagnosis, but none of them fit our problem in the plaster production process discussed here. Most fault diagnosis approaches in the literature only considered a particular control chart, often X-bar or R (range) chart, to examine the process changes (mean or variance). However, in practice, in many processes, it is required to combine the two charts as multiple assignable causes may occur [28]. On the other hand, identification of unnatural patterns combined with specific knowledge of the process results in a more targeted diagnosis. Unfortunately, none of the CCP recognition models in the literature provide this combination automatically, which can be valuable for diagnostic purposes. Moreover, the performance of the model was not evaluated when developing these approaches in a real case study.

Yet, the common problem reported in these studies is the inability to recognize various single and concurrent CCPs, as well as a high rate of false recognition [4,29]. On the other hand, most applications of NNs and ESs to CCP recognition do not obtain more detailed information about the patterns and their change point (when these patterns are observed on control charts). This information is essential for practical assignable cause analysis and, in turn, accelerates the accomplishment of proper remedial activities [21].

Therefore, in this paper, designing a hybrid fault diagnosis system is proposed using NNs and a rule-based ES to help the quality control personnel in recognizing the roots of deviations, and in taking needed predictive or corrective actions. In the design process, for the structure of the NN, a modular approach comprising an LVQ network and seven multilayer perceptrons (MLPs) is used. Therefore, our work provides a neural expert system in intelligent real-time monitoring and predictive, corrective, and remedial diagnosis of process control in plaster production. To develop the proposed neural expert system (Figure 2), we address the following notable features of the model:


The idea is to have, instead of a passive descriptive model, a smart predictive model to assist quality control engineers for the fault diagnosis of the process, particularly from a practical perspective regarding a Quality 4.0 era.

**Figure 2.** Combination of NN and ES: A neural expert system.

#### *1.3. Contribution to Industry 4.0*

Quality "4.0" is a branch of the Industry 4.0 (I4.0) movement associated with the digital transformation process connected with emerging technologies. Quality 4.0 could be defined as the application of Industry 4.0 technologies to quality managemen<sup>t</sup> methods and tools [30]. According to Reference [31], "Quality 4.0 does not replace traditional quality methods, but rather builds and improves upon them". This concept covers all issues of advanced quality managemen<sup>t</sup> in the digital era [32]. For quality (technology, processes, and people), Industry 4.0 enables the transformation of existing capabilities (culture, management, collaboration, and competencies) to drive value [31].

The impact of I4.0 on manufacturing is beyond just the physical production of goods, involving targeting all processes and functions to achieve flexibility, smartness, cost-e ffectiveness, and resilience. Artificial intelligence and machine learning are among the aforementioned technologies that can be utilized to enhance the quality as the heart of smart manufacturing [12,33,34].

On the other hand, construction projects face different sources of disruptions, as they are time-limited, expert-dependent, and highly influenced by process fluctuations caused by weather conditions, material quality, etc., which leads to a high level of complexity and uncertainty in the construction ecosystem [35]. Industry 4.0 challenged the construction industry ecosystem by demonstrating the construction digitalization potential for real-time data collecting, processing, and sharing tools to enhance alignment between demand and supply [35,36].

SPC is an essential tool to monitor process disruptions, safety assurance, and reliability analysis in construction projects [37]. Industry 4.0, with its automation, connectivity, and digital access capacity, is anticipated to be capable of increasing the e fficiency and productivity of SPC. This could happen through enabling intelligent monitoring and diagnosis, automatic tracking of equipment and material, and real-time decision-making, especially in situations where the process is becoming more volatile and complex (Figure 3) [12,30].

**Figure 3.** Quality 4.0: integration of traditional statistical process control (SPC) with Industry 4.0.

Digitalization and automation are the two pillars of smart manufacturing [38]. In this work, our e ffort was to develop a model based on traditional quality systems driven by disruptive technology. In this paper, artificial intelligence in the form of ANN and ES is employed. This proposes new values for the value chain of the manufacturing system on the factory floor level.

In fact, the innovation associated with digitalization, automation, communication, optimization, and customization of Industry 4.0 concepts and trends allows for real-time analysis and interpretation of production, industries, and service processes to improve quality by detecting failures and justifying possible causes while staying competitive in volatile business environments [30,39]. This work provides just a step to move forward and make the dream of "smart manufacturing" happen under the light of Industry 4.0.

The remainder of this paper is organized as follows: Section 2 concisely outlines the methodology of the research; Section 3 presents the proposed model; Section 4 describes the detailed structure of the model; Section 5 presents a comparative analysis and shows some results from a real case study; finally, Section 6 concludes the paper.

#### **2. Materials and Methods**

The methodology of this research is descriptive–experimental research, which is a systematic mapping study based on Reference [40] and an implemented case study in a plaster production plant. Figure 4 represents the schematic diagram depicting the proposed procedure. Using the review of the literature and benchmarking on the extraction of intelligent models used in process control, the structure of a hybrid fault diagnosis system using ANN and ES in the process control of plaster production is presented in this research. Mapping is used to present structuring to synthesize the three main research areas that include statistical process control, neural networks, and expert systems in this research. The case study, based on experiences from model implementation and validation in a plaster production plant, is reflected in Section 4. The plaster production process, which was selected as a case study, is a fluctuating process that has many influencing parameters. On the other hand, because the final production, i.e., construction plaster or so-called "plaster of Paris (PoP)", is mixed in a silo, the monitoring and modification processes in a short period can prevent the entire silo storage product from crashing. On the contrary, if the process is not monitored with statistical process control over an extended period, non-compliance of part of the product with the standard can crash the entire stored production. For example, in the case of filling more than 10% of the silo from a mismatched product, the whole product in the silo will crash. In order to improve the process quality, a survey was done of experts using a questionnaire and interviews to identify critical control parameters. The "initial setting time" of plaster was detected as the critical parameter of the production process. The initial setting time is dependent on the "crystal water" of baked plaster and ought to last between 7 and 15 min in the intended case study. The acceptance range in our case study was between low (LSL = 5.0) and upper (USL = 5.08) specification limits. The process was deemed in control with the lower and upper control limits of LCL = 5.26 and UCL = 5.56. Then, based on existing records, causes of process failures and defects in construction plaster, which were connected with the plaster's qualitative characteristics, were examined using a "cause and e ffect" diagram" [41]. Finally, parameters that could improve customer satisfaction after identifying and prioritizing the foreseeable failure modes were determined and analyzed applying failure mode and e ffects analysis (FMEA) [42]. The statistical population of this research comprised "PoP", which is baked at a particular time in the "low burn" kiln and moved from baking salon to storage silo. The sampling method was a stratified random sampling method. Because of the characteristics of the plaster production process and consistent with the background studies, 25 subgroups of *n* = 125 samples were taken from multiple samples from di fferent shifts. In this research, data were analyzed using three approaches of FMEA, ANN, and discriminant analysis (DA) [1]. To perform discriminant analysis, an understandable database for "SAS" software using Excel software was provided, and discriminant analysis was performed using programming ("Proc Discrim") in SAS software. For the case study of the present study, the data related to the critical parameter of the process were firstly collected, and the causes of product failure were identified and prioritized. Then, given that the proposed model is an intelligent hybrid model that can learn the patterns from input data (samples) using the power of learning neural networks, data were detected. Finally, the error of identifying training and test datasets was compared with the statistical method of discriminant

analysis. In this research, in order to monitor and troubleshoot the process, a model for combining SPC and artificial intelligence was designed using "MATLAB" software. The program codified in MATLAB is able to produce, present, and quickly encode neural network input data, as well as execute expert system rules. The program itself can also perform traditional SPC operations.

**Figure 4.** The flow diagram of the study procedure.

#### **3. The Proposed Hybrid Fault Diagnosis Model**

Based on what was said earlier, this research is based on the integration of NNs and ESs to provide analysis and interpretation for CCPs. The main focus of this study is to introduce a neural expert system-based pattern identifier, which will allow identifying abnormal patterns in order to correct their assignable causes. The operator will be warned if an abnormal pattern occurs in the process. By replacing human skills with a detection algorithm, human intervention is greatly reduced, and an intelligent manufacturing environment could be achieved. In this study, NNs are used to recognize control chart patterns, and an expert system is also used to interpret the identified pattern and determine the causes of the abnormal pattern. The general model of the research is depicted in Figure 5. As Figure 6 indicates, the proposed system consists of three subsystems:


Overall, the model design structure can be divided into three stages of neural network creation, expert system development, and integration of neural network and expert system, as explained below.

**Figure 5.** The structure of the neural expert system.

(**b**) 

(**c**) 

**Figure 6.** (**a**) SPC subsystem; (**b**) pattern recognition subsystem; (**c**) reasoning subsystem.
