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The aim of this paper is to undertake analysis and comparison of the closed‐loop and sensorless control systems sensitivity to the broken rotor for diagnostic purposes. For the same vector control system induction motor drive analysis concerning operation with the asymmetric motor, broken rotor fault handling and operation were investigated. Reliability, range of stable operation, fault symptoms and application of diagnosis methods based on control system variables utilization was analyzed.

Induction motor drive vector control system synthesis was applied using the multiscalar variables of the machine model with nonlinear feedback linearization applied to use classical cascaded PI controllers for the speed‐torque and flux decoupled control. Speed observer was applied for the rotor flux and rotor speed estimation for the sensorless control system synthesis.

Relative sensitivity of the state and control system variables to broken rotor fault based on experimental results for the closed‐loop and sensorless control systems is presented and compared. Drawbacks of using the MCSA analysis for the rotor fault diagnosis in the closed‐loop and sensorless control systems are pointed. Advantages and drawbacks of the state space estimators filtering characteristics in the sensorless control system are described.

Asymmetric IM motor drive handling and diagnosis. Broken rotor range diagnosis inconsistency using the popular MCSA method should be considered in the closed‐loop and sensorless control system of the induction motor drive. Depending on the IM motor drive application and the operation requirements the results can be used for asymmetric machine proper handling, choosing proper control system structure and control system variables for rotor fault early diagnosis.

Sensitivity of the state and control system variables to broken rotor fault based on experimental results for the closed‐loop and sensorless control systems is presented, which implies motor handling procedures and fault diagnosis.

The purpose of this paper is to investigate the need for a universal method for sensorless controlled induction motor drive diagnosis. The increasing number of sensorless control systems in industrial applications require a universal method for the drive diagnosis, which provides reliable diagnostic reasoning independent of control system structure and state variables measurement or estimation method.

Simulations and experimental investigation has been done with assumptions of multiscalar control system as a generalized vector control method, voltage source inverter application, sensorless control system based on selected speed observer structure and squirrel cage induction motor. Broken rotor symptoms are analyzed in the state variables and control system variables using DSP processing without outside measurement devices.

Symptoms of rotor asymmetry caused by broken rotor in the state and control variables was identified and symptoms amplitudes were compared. Based on the simulation and experimental results a new diagnosis method was proposed.

For early broken rotor detection there is a need to identify variables most sensitive to rotor asymmetry. In closed‐loop operation broken rotor symptom signals amplitudes are changed due to control system influence and in sensorless control due to used estimator frequency characteristics. The proposed method assumption is to aggregate symptoms in variables that altogether give results for broken rotor range regardless of applied control system structure or state variable estimator.

This paper shows control system influence to rotor fault symptom amplitudes in the state and control system variables. Identified phenomena is used for a new diagnosis method development.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Presents a real‐time neural network‐based condition monitoring system for rotating mechanical equipment. At its core is an ARTMAP neural network, which continually monitors machine vibration data, as it becomes available, in an effort to pinpoint new information about the machine condition. As new faults are encountered, the network weights can be automatically and incrementally adapted to incorporate information necessary to identify the fault in the future. Describes the design, operation, and performance of the diagnostic system. The system was able to identify the presence of fault conditions with 100 percent accuracy on both lab and industrial data after minimal training; the accuracy of the fault classification (when trained to recognize multiple faults) was greater than 90 percent.

No fault found (NFF) in maintenance has been a frequently observed problem in industrial sectors, but very few academic contributions are devoted to reviewing and summarizing the related research. Considering the growing interest of academicians in NFF during the last decade, there is a critical need to examine theme evolution in this field, most influential authors, contemporary practices, research gaps and proposed solutions.

A portfolio of 169 articles published between 1982 and 2020 was collected from the Scopus database and was systematically analyzed using a two-tier method. First, the evolution, current state of literature and research clusters are identified using bibliometric techniques. Finally, the research clusters are studied to understand the literature's main themes and develop the future research agenda using content analysis.

The results indicate that publications on NFF are rising quickly in the last decade, especially after 2010. The previous NFF research primarily focuses on system design, fault diagnostics, reliability engineering, data management and human factors, but the criticality of economic and risk analysis has not been significantly represented.

The study resulted in developing an inclusive framework and identifying six research clusters that will help in granular understanding, benefit the researchers, practitioners and policy formulators in NFF.

This study examines the NFF's current research direction and calls for further research in integrating NFF economics on its stakeholders like manufacturers, supply chain, customers and risk analysis during the product life cycle.

The purpose of this paper is to illustrate the application of neural network approach to analyze machine's behaviour quantitatively.

The model is developed based on real plant‐data from a variable speed drive centrifugal type pump. The best model settings are recorded and tested for another similar unit in the vicinity to check its generalization capabilities. Owing to the absence of faulty data, this model is tested against preventive maintenance data that show symptoms of abnormality that are seemingly undetected in existing monitoring and control systems. The paper systematically summarizes published literature and suggests suitable network architecture and its capabilities by illustrative example from oil export pumps from an oil and gas offshore production facility.

Artificial intelligent techniques provide a robust platform in providing useful information about system health and sub‐optimal performance.

In any industry, unexpected equipment downtime in principal questions the overall technical integrity of the platform raising major economical concerns. In the Oil & Gas sector, production platforms are in a 24/7 run mode, and thus undergoing major re‐engineering processes by improving existing surveillance and control techniques of their asset. Machine degradation and abnormalities gradually affect performance and in some cases these are not visible in existing condition monitoring (CM) schemes. Recently, there has been an increasing demand for testing and implementing intelligent techniques as a subsidiary to existing CM programs to monitor and assess system's health. Artificial neural networks have emerged as one of the most promising technique in this regard.

The proposed methodology highlights how healthy data from a system can be effectively modelled to identify significant abnormalities. This paper will be useful for experts working in the area of maintenance engineering to early identify state of the system performance.

The objective of this paper is to provide a brief review of recent developments in the area of applications of ANN, Fuzzy Logic, and Wavelet Transform in fault diagnosis. The purpose of this work is to provide an approach for maintenance engineers for online fault diagnosis through the development of a machine condition‐monitoring system.

A detailed review of previous work carried out by several researchers and maintenance engineers in the area of machine‐fault signature‐analysis is performed. A hybrid expert system is developed using ANN, Fuzzy Logic and Wavelet Transform. A Knowledge Base (KB) is created with the help of fuzzy membership function. The triangular membership function is used for the generation of the knowledge base. The fuzzy‐BP approach is used successfully by using LR‐type fuzzy numbers of wavelet‐packet decomposition features.

The development of a hybrid system, with the use of LR‐type fuzzy numbers, ANN, Wavelets decomposition, and fuzzy logic is found. Results show that this approach can successfully diagnose the bearing condition and that accuracy is good compared with conventionally EBPNN‐based fault diagnosis.

The work presents a laboratory investigation carried out through an experimental set‐up for the study of mechanical faults, mainly related to the rolling element bearings.

The main contribution of the work has been the development of an expert system, which identifies the fault accurately online. The approaches can now be extended to the development of a fault diagnostics system for other mechanical faults such as gear fault, coupling fault, misalignment, looseness, and unbalance, etc.

The purpose of this paper is to detect gears' faults with an automatic decision‐making process and find a reliable method to detect faults on gear systems using a composition of conventional methods.

First, the vibration behavior of gears during engagement is investigated. Then, after studying different methods of fault detection using vibration signals analysis, a suitable method is proposed for the case of gears. For this purpose, a fuzzy model is employed based on available knowledge about fault detection of gears and results obtained from vibration behavior of gears. In the mentioned fuzzy model, a feature extracted from wavelet transform and also a couple of statistical indexes are used as fault criteria.

Using fuzzy systems instead of numerous data in training the decision‐making system and also utilizing available knowledge of gears' signals and information of fault effects can significantly simplify the decision‐making process in auto‐detecting gears faults, considering difficulty of laboratory set up, manufacturing and different faults creation, as well as, lack of sufficient data.

In order to validate and enhance the proposed model, an empirical set up is manufactured and tested. Later on, the model is tested on another set of gears.

Although the gears' faults were completely different from those of experimental set up, promising results in detecting faults were obtained. Moreover, it is shown that it is possible to determine the level of gears' health, as well as to estimate the gears' status, owing to fuzzy logic. This issue can be observed in the change of fault parameter while analyzing signals related to the fault growth in gears.

Delays in construction projects are both disruptive and expensive. Thus, potential causes of schedule deviation need to be identified and mitigated. In previous research, delay factors were predominantly identified through surveys administered to stakeholders in construction projects. Such delay factors are typically considered individually and presented at the same level without explicitly examining their sequence of occurrence and inter-relationships. In reality, owing to the complex structure of construction projects and long execution time, non-conformance to schedule occurs by a chain of cascading events. An understanding of these linkages is important not only for minimising the delays but also for revealing the liability of stakeholders. To explicitly illustrate the cause–effect and logical relationship between delay factors and further identify the primary factors which possess the highest significance toward the overall project schedule delay, the fault tree analysis (FTA) method, a widely implemented approach to root cause problems in safety-critical systems, has been systematically and rigorously executed.

Using a case study, the in-depth analysis for identifying the most fundamental delay factors has been fulfilled through FTA's tree structure. The logical deduction for mapping and visualising the chronological and cause–effect relationships between various delay factors has been conducted through the logical gate functions of FTA based on the data collected from the site event log, pre-fabricated structural component manufacturing log and face-to-face interview with project stakeholders.

The analysis identified multiple delay factors and showed how they are linked logically and chronologically from the primary causes to the ultimate undesired event in a rigorous manner. A comparison was performed between the proposed FTA model and the conventional investigation method for revealing the responsibility employed in the construction industry, consisting of event logs and problem reports. The results indicate that the FTA model provides richer information and a clearer picture of the network of delay factors. Importantly, the ability of FTA in revealing the causal connection between the events leading to the undesired delays and in comprehending their prominence in the real-world construction project has been clearly displayed.

This study demonstrates a new application of FTA in the construction sector allowing the delay factors to be understood and visualised from a new perspective. The new approach has practical use in finding and removing root causes of the delay, as well as clarifying the attribution of responsibility that causes the delay.