Critical Industrial Processes Research Articles

Auxiliary ligand influence on interpenetration and C2H2/CO2 separation in NDI based isoreticular MOFs

Selective separation of C2H2 from CO2 mixtures promises a critical industrial process to produce high-purity C2H2 but challenging due to their similar physical properties and molecular sizes/shapes. Naphthalenediimide (NDI) ligand-based metal − organic frameworks (MOFs) exhibit potential in selective adsorption of C2H2 due to NDI’s high affinity to C2H2 molecules. Here, three iso-reticular NDI based MOFs (denoted as FJU-109, 110 and 111) are obtained by tuning interpenetration using auxiliary ligands of different lengths. Based on the actual column breakthrough experiments, FJU-111 can achieve separation C2H2/CO2 mixture, with a separation selective of 2.2. Calculation of the binding energy shows that C-H…O interactions between guest and O atom from NDI ligand play a dominant role in elective recognition of acetylene.

Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning Model for IIoT Environment

Intrusion Detection in the Industrial Internet of Things (IIoT) concentrations on the security and safety of critical structures and industrial developments. IIoT extends IoT principles to industrial environments, but linked sensors and devices can be deployed for monitoring, automation, and control of manufacturing, energy, and other critical systems. Intrusion detection systems (IDS) in IoT drive to monitor network traffic, device behavior, and system anomalies for detecting and responding to security breaches. These IDS solutions exploit a range of systems comprising signature-based detection, anomaly detection, machine learning (ML), and behavioral analysis, for identifying suspicious actions like device tampering, unauthorized access, data exfiltration, and denial-of-service (DoS) attacks. This study presents an Improving Intrusion Detection using Satin Bowerbird Optimization with Deep Learning (IID-SBODL) model for IIoT Environment. The IID-SBODL technique initially preprocesses the input data for compatibility. Next, the IID-SBODL technique applies Echo State Network (ESN) model for effectual recognition and classification of the intrusions. Finally, the SBO algorithm optimizes the configuration of the ESN, boosting its capability for precise identification of anomalies and significant security breaches within IIoT networks. By widespread simulation evaluation, the experimental results pointed out that the IID-SBODL technique reaches maximum detection rate and improves the security of the IIoT environment. Through comprehensive experimentation on both UNSW-NB15 and UCI SECOM datasets, the model exhibited exceptional performance, achieving an average accuracy of 99.55% and 98.87%, precision of 98.90% and 98.93%, recall of 98.87% and 98.80%, and F-score of 98.88% and 98.87% for the respective datasets. The IID-SBODL model contributes to the development of robust intrusion detection mechanisms for safeguarding critical industrial processes in the era of interconnected and smart IIoT environments.

STAGED: A Spatial-Temporal Aware Graph Encoder–Decoder for Fault Diagnosis in Industrial Processes

Data-driven fault diagnosis for critical industrial processes has exhibited promising potential with massive operating data from the supervisory control and data acquisition system. However, automatically extracting the complicated interactions between measurements and subtly integrating it with temporal evolutions have not been fully considered. Besides, with the increasing complexity of industrial processes, accurately locating fault roots is of tremendous significance. In this article, we propose an unsupervised spatial-temporal aware graph encoder-decoder (STAGED) model for industrial fault diagnosis. Firstly, the high-dimensional measurements are constructed as a weighted graph to depict the complicated interactions. Then, the graph convolutional network, long short-term memory network and attention mechanism are applied to learn a comprehensive representation for multi-series. To enforce the model to better capture the temporal evolution, the dual decoder that performs reconstruction and prediction tasks simultaneously is adopted with a well-designed comprehensive loss function. By learning the spatial-temporal evolutions of datasets, faults can be diagnosed and located at a fine-grained level based on reconstruction deviations. To verify the performance of STAGED, experiments on Cranfield three-phase flow facility and secure water treatment datasets are implemented and the results indicate that it can provide insight into fault evolution and accurately diagnose faults.

FC-PV-battery-Z source-BBO integrated unified power quality conditioner for sensitive load & EV charging station

The proposed integrated system combines of fuel cell (FC), photovoltaic (PV), battery, Z-source and Biogeography-Based Optimization (BBO) based Unified Power Quality Conditioner (UPQC) compensator aims to provide a high-quality power supply to sensitive loads, such as electrical, electronics, medical equipment, Electrical Vehicles (EV), or critical industrial processes. It can regulate voltage, mitigate power disturbances (e.g., voltage sags, swells, flicker, voltage interruption, harmonics, reactive power etc.), and ensure a stable power supply. Additionally, it can facilitate EV charging stations by managing the power flow and optimizing charging processes. The PV or FC based Z-source inverter can control and regulate power flow in both directions. It operates with a unique impedance network, allowing for voltage boosting or bucking. The Z-source inverter enhances the flexibility and control of power flow in the system. The fuel cell generates electricity through an electrochemical process, typically using hydrogen as a fuel source, while the PV array converts sunlight into electrical energy. The FC-PV system provides a renewable and clean energy source. The battery serves as an energy storage device within the integrated UPQC system. It stores excess energy produced by the FC-PV system or from the grid during off-peak hours and releases it when needed, contributing to load balancing and stability. Biogeography-Based Optimization (BBO)-based Selected Harmonic Elimination Pulse Width Modulation (SHE-PWM) technique calculate the proper inverter switching angles and control the total harmonic distortion (%THD). Grid connected EV charging station produce nonlinear current and pollute the DC-link system. The proposed FC-PV-Z source-BBO based UPQC and its control strategy can enhance the EV-charging induced PQ (power quality) issues, eliminate the voltage and current harmonic or reduce overall voltage and current %THD control the proper reactive power, improve the power factor, restoring the flicker, transient, swells and sags of the micro grid, grid or smart grid and completely moderate the fluctuations of DC-link voltage.

A regularized lattice Boltzmann model with filter for multiphase flow with diffusion-dominated mass transfer considering two-film theory

Complex flow, considering the interfacial mass transfer with the two-film theory, is always encountered in critical industrial processes. The phase-field lattice Boltzmann method (PFLBM) coupling with the revised Fick's law mass transfer convection–diffusion equation (CDE) is a practical approach to predict the bulk concentration distribution in two-phase flows. However, solutions of concentration have oscillations and even diverge near the sharp gradient when the relaxation time of governing equations is close to 0.5 (i.e., diffusion-dominated). In this paper, an integrated PFLBM model considering two-phase flow and interfacial mass transfer with a new filtering algorithm and collision operator was built to extend the wider range of the existing model for the two-film CDE with an extremely low diffusion coefficient. First, the two-film mass transfer model from our team was furthermore developed with the second-order formation to meet the high precision of concentration on two-phase interfaces. Then, directional filtering algorithms and regularized-finite-difference (rLBM-FD) collision operator were introduced to improve the numerical stability and limit the numerical diffusion. Four common collision operators were implemented and thoroughly tested in two cases to verify the robustness and accuracy of our new model. In conclusion, the combination of the rLBM-FD with standard non-linear filter reaches the highest robustness, mass-conservativeness, and limitation on numerical diffusion. The directional non-linear filter has the lowest computational cost of any microscopic variable filter and can increase the robustness by two times. Macro-variable filtering is not appropriate for treating the two-film equilibrium because the mass loss and robustness are unacceptable.

Microbial electrosynthetic nitrate reduction to ammonia by reversing the typical electron transfer pathway in Shewanella oneidensis

Ammonia production is a critical industrial process, and mild routes to recycle nitrates in wastewater could be a promising route to ammonia synthesis. In this study, ammonia production is demonstrated in a microbial electrosynthesis system with nitrate and an electrode as electron acceptors and donors, respectively. Based on the bidirectional extracellular electron transfer capability of Shewanella oneidensis MR-1, our microbial electrosynthetic system achieves a maximum ammonia production rate of 24.3 μg h−1·mg protein−1 with 82.5% selectivity and 33.1% cathodic efficiency and functions for several cycles over 30 days. Electrochemical analysis suggests that cytochromes c, flavins, and the flavin/c-cytochrome combination play a pivotal role. Charge transfer resistance weakens over the course of weeks, resulting in easier electron transfer. Parallel reaction monitoring proteomics suggest that reversing a typical “Mtr pathway” plays a role, and a dissimilatory nitrate to ammonia pathway is used. This work proposes a progressive route to carry out ammonia synthesis under mild conditions.

Highly selective electrocatalytic alkynol semi-hydrogenation for continuous production of alkenols

Alkynols semi-hydrogenation is a critical industrial process as the product, alkenols, have extensive applications in chemistry and life sciences. However, this class of reactions is plagued by the use of high-pressure hydrogen, Pd-based catalysts, and low efficiency of the contemporary thermocatalytic process. Here, we report an electrocatalytic approach for selectively hydrogenating alkynols to alkenols under ambient conditions. For representative 2-methyl-3-butene-2-ol, Cu nanoarrays derived electrochemically from CuO, achieve a high partial current density of 750 mA cm−2 and specific selectivity of 97% at −0.88 V vs. reversible hydrogen electrode in alkaline solution. Even in a large two-electrode flow electrolyser, the Cu nanoarrays deliver a single-pass alkynol conversion of 93% with continuous production of 2-methyl-3-butene-2-ol at a rate of ~169 g gCu−1 h−1. Theoretical and in situ electrochemical infrared investigations reveal that the semi-hydrogenation performance is enhanced by exothermic alkynol adsorption and alkenol desorption on the Cu surfaces. Furthermore, this electrocatalytic semi-hydrogenation strategy is shown to be applicable to a variety of alkynol substrates.

Design and Implementation of a Ground Power Unit Based on the Fault-Tolerant Inverter

Due to the widespread use of static power converters in various industries and sensitive applications such as ground power units (GPUs), it is essential that the inverters have high reliability. A failure in the inverters can cause undesirable consequences such as; low performance, shutting down the system, and losing the stability of the entire system. On the other hand, in some critical industrial processes with high standstill cost and safety-aspect concerns, such as aircrafts, high reliability, and survivability of the system are very important. Therefore, the use of fault-tolerant (FT) inverters is inevitable in such applications. In this paper, using an FT inverter, the reliability of the GPUs is increased and it results in higher availability and continuous system performance. Furthermore, a simple automatic fault-detection (FD) algorithm is presented by taking the inverter pole voltage as a fault detection parameter. The inverter is analyzed for open-circuit (OC) fault in the switches and the reliability of the FT inverter is compared with the classic inverter. The simulation and experimental results of the 1 kVA converter prototype are presented to confirm the FT inverter performance and the proposed method.

Design and implementation of ground power unit using a three‐level fault‐tolerant neutral point clamped inverter

AbstractDue to the widespread use of static power converters in various industries and sensitive applications such as ground power units (GPUs), it is essential that the inverters have high reliability. A failure in the inverters can cause undesirable consequences such as low performance, shutting down the system, and losing the stability of the entire system. On the other hand, in some critical industrial processes with high standstill costs and safety‐aspect concerns, such as aircraft, high reliability, and survivability of the system, are very important. Therefore, the use of fault‐tolerant (FT) inverters is inevitable in such applications. In this paper, using a three‐level fault‐tolerant neutral point clamped (FT‐NPC) inverter, the reliability of the GPUs is increased and it results in higher availability and continuous system performance. Furthermore, a simple automatic fault detection (FD) algorithm is presented by taking the inverter pole voltage as a FD parameter. The inverter is analyzed for open‐circuit fault in the switches, and the reliability of the FT‐NPC inverter is compared with the classic inverter. The simulation and experimental results of the 2‐kVA converter prototype are presented to confirm the FT inverter performance and the proposed method.

Highly permeable and acid-resistant nanofiltration membrane fabricated by in-situ interlaced stacking of COF and polysulfonamide films

The fabrication of nanofiltration (NF) membranes with excellent acid resistance and high separation performance remains a tremendous challenge due to the lack of precise membrane structure manipulation. Herein, covalent organic frameworks (COFs), due to their abundant porosity and highly ordered structures, are employed to synthesize high-permeation nanofiltration membranes. An acid-stable COF layer and a polysulfonamide (PSA) layer were fabricated by in-situ interfacial polymerization (IP) upon the polyethersulfone (PES) ultrafiltration substrate in turns. The acquired COF-based composite membrane exhibited sub-nanometer pore size and excellent rare-earth ions separation performance due to the interlaced stacking between the COF and PSA layers, as well as the COF interlayer regulated IP process. Additionally, the composite membrane showed high rejection of >92.2% for trivalent rare-earth ions (RE3+) and high water permeance of >43.3 L h−1 m−2 bar−1 at both pH = 6.8 and pH = 1, the superior separation performance can be ascribed to the high porosity and abundant transportation pathway provided by the COF layer and the interlaced stacking structure between the COF layer and the PSA layer. The facile membrane fabrication procedure, along with the excellent water permeation performance and acid resistance, render the composite membrane in this study applicable for a broad range of critical industrial and environmental processes.

A novel approach for accurate detection of the DDoS attacks in SDN-based SCADA systems based on deep recurrent neural networks

Supervisory Control and Data Acquisition (SCADA) systems supervise and monitor critical infrastructures and industrial processes. However, SCADA systems running on conventional network architecture have scalability and manageability limitations. Through its programmable dynamic architecture, Software Defined Network (SDN) technology offers rapid configuration, scalability, and better manageability for SCADA systems. Combining existing SCADA systems with SDN has produced more practical SDN-based SCADA systems. However, due to their sensitive positions, SCADA systems are the targets of highly dangerous cyberattacks. In particular, failure to accurately detect and take action against cyberattacks like Distributed Denial of Service (DDoS) may lead to service disruption in SDN-based SCADA systems which may cause loss of life or massive financial losses. This study suggested the Recurrent Neural Network (RNN) classifier model, including two separate parallel deep learning-based methods, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to better the detection of DDoS attacks targeting SDN-based SCADA systems. The proposed parallel structure was trained from end to end with a training dataset and tested with the validation dataset. This model was processed in the transfer learning procedure. The features were extracted with the training dataset, and the extracted features were classified with Support Vector Machines (SVM). While in transfer learning, the validation data was used in feature extraction and obtained features were classified with a trained SVM classifier. As part of the work, a sample dataset containing both DDoS attacks and regular network traffic data was created using an experimentally generated SDN-based SCADA topology. While experimental works yielded an accuracy of 97.62% for DDoS attack detection, transfer learning allowed a performance improvement of around 5%. The results have shown that the proposed RNN deep learning classifier model can effectively detect DDoS attacks targeting SDN-based SCADA systems.

Metal-Organic Framework Based Hydrogen-Bonding Nanotrap for Efficient Acetylene Storage and Separation.

The removal of carbon dioxide (CO2) from acetylene (C2H2) is a critical industrial process for manufacturing high-purity C2H2. However, it remains challenging to address the tradeoff between adsorption capacity and selectivity, on account of their similar physical properties and molecular sizes. To overcome this difficulty, here we report a novel strategy involving the regulation of a hydrogen-bonding nanotrap on the pore surface to promote the separation of C2H2/CO2 mixtures in three isostructural metal-organic frameworks (MOFs, named MIL-160, CAU-10H, and CAU-23, respectively). Among them, MIL-160, which has abundant hydrogen-bonding acceptors as nanotraps, can selectively capture acetylene molecules and demonstrates an ultrahigh C2H2 storage capacity (191 cm3 g-1, or 213 cm3 cm-3) but much less CO2 uptake (90 cm3 g-1) under ambient conditions. The C2H2 adsorption amount of MIL-160 is remarkably higher than those for the other two isostructural MOFs (86 and 119 cm3 g-1 for CAU-10H and CAU-23, respectively) under the same conditions. More importantly, both simulation and experimental breakthrough results show that MIL-160 sets a new benchmark for equimolar C2H2/CO2 separation in terms of the separation potential (Δqbreak = 5.02 mol/kg) and C2H2 productivity (6.8 mol/kg). In addition, in situ FT-IR experiments and computational modeling further reveal that the unique host-guest multiple hydrogen-bonding interaction between the nanotrap and C2H2 is the key factor for achieving the extraordinary acetylene storage capacity and superior C2H2/CO2 selectivity. This work provides a novel and powerful approach to address the tradeoff of this extremely challenging gas separation.

JTAG-based PLC memory acquisition framework for industrial control systems

In industrial control systems (ICS), programmable logic controllers (PLC) are the embedded devices that directly control and monitor critical industrial infrastructure processes such as nuclear plants and power grid stations. Cyberattacks often target PLCs to sabotage a physical process. A memory forensic analysis of a suspect PLC can answer questions about an attack, including compromised firmware and manipulation of PLC control logic code and I/O devices. Given physical access to a PLC, collecting forensic information from the PLC memory at the hardware-level is risky and challenging. It may cause the PLC to crash or hang since PLCs have proprietary, legacy hardware with heterogeneous architecture. This paper addresses this research problem and proposes a novel JTAG (Joint Test Action Group)-based framework, Kyros, for reliable PLC memory acquisition. Kyros systematically creates a JTAG profile of a PLC through hardware assessment, JTAG pins identification, memory map creation, and optimizing acquisition parameters. It also facilitates the community of interest (such as ICS owners, operators, and vendors) to develop the JTAG profiles of PLCs. Further, we present a case study of Kyros implementation over Allen-Bradley 1756-A10/B to help understand the framework's application on a real-world PLC used in industry settings. The sample PLC memory dumps are shared with the research community to facilitate further research.

Securing Industrial Cyber–Physical Systems: A Run-Time Multilayer Monitoring

Industrial cyber-physical systems (ICPSs) are widely deployed in monitoring and control of the nation's critical industrial processes, such as water distribution networks and power grids. ICPSs are the tight integration of cyber (software) and physical entities connected via communication networks. Communication networks are typically realized via wireless channels to reduce the cost of wires and installation. However, they are also inherently unreliable, easy to disrupt, and subvert, which makes them a potential target for cyberattacks. The failure of communication can cause data loss or delays, which can compromise system functionality and have catastrophic consequences due to the strict real-time requirements of ICPSs. Current run-time security monitors protect ICPSs either at communication level (through network intrusion monitors) or at application level (through threat detection monitors). Such monitors are layer-specific and, thus, fail to detect advanced threats arising from the multilayer disruption. In this article, we present a multilayer run-time security monitor that can detect discrepancies caused by interdependent application and communication layer attacks and prevent their propagation into the system's control loops. We demonstrate the effectiveness of the approach via an example of the ICPS used for control and monitoring of a water distribution network.

SudoDEM: Unleashing the predictive power of the discrete element method on simulation for non-spherical granular particles

This paper presents a novel open-source discrete element code, SudoDEM, for efficient modeling of both 2D and 3D non-spherical particles under a GPL v3 or later license. Built upon a popular open-source code YADE, our code inherits the core of a classic DEM framework empowered by OpenMP acceleration, and further offers unique features of a rich library of prime particle shapes, including poly-superellipsoids, superellipsoids, cylinders, cones, polyhedrons for 3D and disks and superellipses for 2D. Unlimited choices of more complex particle shapes can be readily generated by clumping these prime shapes. Efficient modeling of complex shaped particles hinges on contact detection. In SudoDEM, we have developed three generic and efficient contact detection algorithms, the parametric common normal (PCN) algorithm, the Gilbert–Johnson–Keerthi (GJK) algorithm, and the hybrid PCN–GJK algorithm, to handle contacts among complex-shaped particles during a typical DEM simulation. The new DEM code is validated and further showcased by multiple examples, including granular packing, triaxial compression, and landslide, its robustness, efficiency and versatility in providing realistic solutions to granular mechanics problems. The project is hosted at an open-source page at https://sudodem.github.io, while the source codes are freely available at a GitHub repository (https://github.com/SudoDEM). We foresee a great capability and potential for SudoDEM in advancing future progress in granular physics and granular mechanics and in fostering advanced simulations of critical engineering and industrial processes pertaining to granular media. Program summaryProgram title: SudoDEMCPC Library link to program files:https://doi.org/10.17632/brpk4g28zn.1Developer’s repository link:https://github.com/SudoDEM/SudoDEM, https://sudodem.github.ioLicensing provisions: GNU General Public License 3Programming language: C++, PythonNature of problem: Grain shape underpins important aspects of the physical and mechanical behaviors of granular media. The inability of realistic and robust modeling of particle shape has been a major obstacle for discrete-based numerical methods in solving practical problems of granular materials.Solution method: SudoDEM implements three generic algorithms of contact detection among non-spherical particles in Discrete Element Method, which provides a rich library of particle shapes available for DEM modeling of granular media.

Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices

Cyber-threat protection is today's one of the most challenging research branches of information technology, while the exponentially increasing number of tiny, connected devices able to push personal data to the Internet is doing nothing but exacerbating the battle between the involved parties. Thus, this protection becomes crucial with a typical Internet-of-Things (IoT) setup, as it usually involves several IoT-based data sources interacting with the physical world within various application domains, such as agriculture, health care, home automation, critical industrial processes, etc. Unfortunately, contemporary IoT devices often offer very limited security features, laying themselves open to always new and more sophisticated attacks and also inhibiting the expected global adoption of IoT technologies, not to mention millions of IoT devices already deployed without any hardware security support. In this context, it is crucial to develop tools able to detect such cyber threats. In this article, we present Passban, an intelligent intrusion detection system (IDS) able to protect the IoT devices that are directly connected to it. The peculiarity of the proposed solution is that it can be deployed directly on very cheap IoT gateways (e.g., single-board PCs currently costing few tens of U.S. dollars), hence taking full advantage of the edge computing paradigm to detect cyber threats as close as possible to the corresponding data sources. We will demonstrate that Passban is able to detect various types of malicious traffic, including Port Scanning, HTTP and SSH Brute Force, and SYN Flood attacks with very low false positive rates and satisfactory accuracies.

Analysis and Realization of CAN Bus Technology in Industrial Conveyors

Automation industry has undergone a drastic development in recent years. In this busy world human intervention in very high critical industrial processes are compensated by the enormous growth of the automation industries. Due to the advancement there is no need for a person to continuously monitor the system instead by implementing automation through latest technologies, a very high efficient operation of the system is possible. Conveyor system plays an important role in automation for material handlin g and packaging. For the movement of the material, that is transportation, in any processing, the lack of safety factor leads to many limitation in the automation, due to the unachievable efficiency, that is operational efficiency. For that variable speed drive are used for the purpose of modeling the conveyor belt speed, which is controlled through programmable logic controller. The main objective is to develop an embedded system that will automate the boiler control system by monitoring and controlling the temperature and pressure using the CAN bus communication technology. A PIC microcontroller is used in the project to control the entire operation of the embedded system.

Architecture assessment for safety critical plant operation using reachability analysis of timed automata

This article deals with the validation of critical industrial process architectures from the point of view of safety and operation. During the engineering phases, the objective is to complement conventional safety studies with an approach that focuses on plant operation. In this context, one of the major challenges is to provide a guarantee that the designed architecture will be able to react safely to critical situations and events.To face the complexity resulting from the large number of functionalities and devices of the installations under consideration, the proposed approach is based on dynamic models of architectures, using the formalism of timed automata and reachability analysis to verify that, given a particular configuration of an architecture, the process can be safely operated to achieve a given objective. The result is a formal tool that allows engineers and plant operators to evaluate architecture safety with different types of dysfunctional scenarios based on their operational safety expertise.The article presents the formal modelling framework, which emphasizes structured modelling using patterns to promote reuse and instantiation over several candidate architectures. The contribution is illustrated and discussed using an experimental laboratory platform.

Secure Self Optimizing Software Defined Framework for NB-IoT Towards 5G

Abstract New operating requirements, new architectures and denser topologies are forcing change and driving more complexities. Self-Optimization, Self-configuration and Self-healing are the important objectives of the mobile-SON which is a software platform operating under network management layer. Cost effective with scalability and high bandwidth technology is required for IoT processes of all kind – from connected vehicles to critical industrial processes with automatic configuration. Scale, speed and cost combined parameters arises the new mobile capabilities of automatic operating processes in RAN and across the wider network. Objective of 5G is to reach any end point over any kind of access network with any kind of service i.e. evolved LTE, legacy RATs, fixed access and IoT applications over numerous access technologies. In converged network, common services such as entertaining video, bulk internet access and any managed contents using any access technology or legacy internet access and IP multimedia sub system (IMS) automatically configured in 5G core. In this article self-optimizing framework for ultra IoT is proposed, which represents the M2M communication, network cloud. Impact of network slicing by SDN controller and SON which plays important role in 5G cellular network architecture is discussed. Reviews of different authors are included regarding current use cases conducted in different countries. Next generation data lakes, data analytics on cloud and data warehouse performance depend on the new technology architecture.

Induction Motors Fault Diagnosis Using Finite Element Method: A Review

Condition monitoring and fault diagnosis of induction motors serve as essential techniques toward the reliable operation of critical industrial processes. The finite element method (FEM) offers a great insight into fundamental principle and physical operation of the machine. It can model complex magnetic circuit topology, discrete windings layouts, and nonlinear magnetic material properties of the machine. It determines the machine parameters (such as the magnetic field distribution, flux density, electromagnetic torques, and stator current) and can model localized magnetic saturation due to faults to a high degree of accuracy. Used as fault detection algorithms, the FEM can address the issues such as the lack of comprehensive fault databases through field measurements, and the difficulty in distinguishing fault severity. It can reduce the number of destructive tests required in the field/labs, simulate any faulty states of the machine. Although FEM has been widely used in induction motors’ design and analysis, its application in fault diagnosis is limited despite the promising potential. In this article, a literature review is conducted on induction motor fault diagnosis techniques using FEM. The state-of-the-art techniques reported in the literature are categorized into three streams: first, FEM-based fault diagnosis approach, second, FEM and signal processing-based approach, and third, FEM, machine learning, and other advanced techniques-based approach. The advantages of fault diagnosis techniques using the FEM are demonstrated and the future research direction is recommended.