Fault Detection and Classification (FDC) Market Size, Share & Trends

Updated on : November 22, 2024

Fault Detection and Classification (FDC) Market Size & Growth

[257 Pages Report] The global fault detection and classification market size was valued at USD 4.4 billion in 2022 and is projected to reach USD 7.4 billion by 2028; growing at  a CAGR of 8.9% during the forecast period 2022 to 2028 Stringent health and safety measures imposed by governments and standards organizations on global manufacturing firms and Strong focus of manufacturers on automating quality control and quality assurance processes are among the factors driving the growth of fault detection and classification industry .

Fault Detection and Classification (FDC) Market Statistics Forecast to 2028

To know about the assumptions considered for the study, Request for Free Sample Report

Fault Detection and Classification (FDC) Market Trends:

Driver: The increased complexity of systems

The rapid advancement of technology has led to increasingly complex systems across various industries. This complexity presents both opportunities and challenges, particularly in maintaining system reliability and efficiency. With increased complexity comes heightened challenges. Traditional manual monitoring and diagnosis no longer suffice, as the intricacies of these systems surpass human capacity. As systems become more intricate, the need for effective fault detection and classification (FDC) becomes paramount. FDC systems typically use a combination of sensors, data analytics, and machine learning algorithms to detect and classify faults. The sensors collect data from the system, which is then analyzed by the data analytics algorithms to identify any deviations from normal operating conditions. By continuously assessing data, these systems establish baselines for normal operation and identify deviations. Upon detecting anomalies, they employ classification algorithms to categorize faults, enabling swift responses.

The relentless rise in the complexity of modern systems necessitates innovative approaches to ensure their seamless functioning. Fault detection and classification systems stand as beacons of technological advancement, offering real-time monitoring, rapid fault identification, and informed decision-making. As industries embrace these solutions, they enhance their operational efficiency, safeguard their personnel and assets, and position themselves at the forefront of a future where complexity is not a challenge but an opportunity. In a world of intricate interconnections, FDC emerges as a cornerstone for sustained progress.

Restraint: Dearth of skilled professionals in manufacturing factories

In the manufacturing industry, there is a tremendous reliance on human experience and human senses. However, recently, the shortage of skilled professionals has become a more critical issue; therefore, automation of manufacturing, assembly, product testing and inspection, and transportation processes that depend on people has become an urgent task for businesses. On account of this, companies across industries embrace industrial automation with the growing use of fault detection and classification systems, machine vision systems, etc., to improve operational efficiency and performance, reduce waste and conserve natural resources, and reach new markets and audiences, but factories embracing machine vision technology would require more complex skill sets, and it could be difficult for lower/semi-skilled, less-educated workers to access opportunities.

Machine vision systems deployed to eliminate the defects require skilled professionals capable of deciphering machine signals on a dashboard and work together with collaborative robots on which machine vision systems are mounted.

Furthermore, economic growth largely depends on the productivity of the workforce. The current challenge is to promote lifelong learning, particularly among the elderly and economically active people. For example, China and Japan will face rapid population aging in the coming decades, necessitating the maintenance, and upgrading of a growing pool of mature and older workers, in addition to making further progress in formal education. Secondly, the growing younger generation will continue to strain both education and training capacities and job creation rates as more young people enter the job market. Young people with low skill levels are having difficulty finding work everywhere. In nearly every industry, there are now more open jobs than people looking for work. There are 1.06 job openings for every unemployed worker in the manufacturing industry. On average, 25% of workers in the manufacturing sector worldwide are underqualified.

Fault detection and classification systems with high-end and frequently changing features necessitate highly skilled individuals. An unskilled worker may be unable to carry out the operations and may impede the operation of the fault detection and classification system, resulting in false detection or a defect going undetected by the system. The shortage of skilled professionals can cause companies in various industries to shy away from fault detection and classification systems, proving a market restraint.

Opportunity: Increasing adoption of artificial intelligence (AI) technology

Quality control is one of the most important factors in manufacturing. Inspecting each product manually is costly, in terms of time and effort, creating bottlenecks caused by delayed production. In many cases, defects can be easily missed by the human eye or even by industry experts, resulting in decreased quality of an individual component or a defective final product that must be scrapped. Defect rates can often increase with more complex manufacturing systems. Recently, manufacturers have been focused on adopting advanced technologies, such as AI and deep learning, on transforming production processes and faster inspection of products and prompt detection of defects. The combination of software, use of deep learning technology, the power of parallel processing, and easy-to-use tools are core parameters of this transformation.

AI-based FDC tools/systems are superior to manual inspection in tracking products on the assembly lines, delivering significantly higher precision rates, enhanced product quality, increased productivity, higher throughput, and lower production costs. AI-based fault detection and classification systems used for quality control utilize machine learning technology so that defect prediction models can autonomously learn and make inferences from the manufacturer’s data. These models can shortlist important features and create new implicit rules to determine which combinations of features impact the overall product quality. Autonomous fault detection and classification systems deliver improved efficiency and accuracy, which constantly adjust to detect new types of defects across industries and verticals. Production yield and customer satisfaction largely depend on AI-based quality control for wide application areas ranging from nanometric semiconductors to huge engine parts of a commercial airplane. Traditional fault detection and classification systems cannot evaluate complex objects or products with high variations as easily as human operators; however, an AI-based fault detection and classification system can effortlessly find and compare defects with high variability.

AI-based fault detection and classification systems provide significant benefits to manufacturers:

• Early error detection, preventing defective parts from moving down the production line
• Increased production volume without sacrificing quality
• Tracking historical data to pinpoint issues and improve future production processes
• Optimized incoming material inspection
• Reaching, and often surpassing, human-level accuracy

These technologies enable the development of sophisticated algorithms capable of learning from historical data and adapting to changing conditions. This leads to higher accuracy in fault detection and classification and reduces false positives.

Furthermore, various companies are heavily investing in R&D activities to offer innovative and technologically advanced products and solutions in the fault detection and classification market. For instance, in July 203, Microsoft collaborated with Birlasoft to Establish Generative AI Centre of Excellence, Shares Rebound After Announcement. Birlasoft will utilize Azure OpenAI Service features for product design, process optimization, quality and defect detection, predictive maintenance, and digital twins for the manufacturing sector

Challenge: Complexity in implementation of fault detection and classification solution and technologies

Increasing competition, growing instability in the business arena, and ongoing technological advancements necessitate to change the way manufacturing enterprises operate and expand their business. To achieve the aforementioned objectives, organizations need to accomplish seamless production. Fault detection and classification systems are used in a variety of applications, including manufacturing, healthcare, and packaging. Each application has its own quirks, and technological advancements add complexity to manufacturing processes on a daily basis. Fault detection and classification systems face the challenge of meeting the diverse and ever-changing needs of various applications. Whether it is a camera, an optics, software, or a frame grabber, versatile solutions can be quickly developed to tackle a wide range of tasks by using multipurpose components. The need of the hour is to simplify the integration process with regard to different components of fault detection and classification systems and the production lines at application sites. With increasing efforts being put into boosting the convenience of installing and handling all technical systems, players in the fault detection and classification market must look to address these requirements by coming up with plug-and-play solutions.

Fault Detection And Classification Market Ecosystem

 Prominent companies in this market include well-established, financially stable providers of fault detection and classification systems. These companies have been operating in the market for several years and possess a diversified product portfolio, state-of-the-art technologies, and strong global sales and marketing networks. Prominent companies in this market include Keyence Corporation (Japan), Cognex Corporation (US), KLA Corporation (US), Teledyne Technologies (US), OMRON Corporation (Japan), Microsoft (US), Tokyo Electron Limited (Japan), Siemens (Germany), Amazon Web Services, Inc. (US), Synopsys, Inc. (US)

By end use, the automotive segment is expected to grow with the highest CAGR from 2023 to 2028

The fault detection and classification market for automotive end use is expected to grow at the highest CAGR from 2023 to 2028. The growth of the segment is attributed to the the increasing complexity of vehicles, stringent safety regulations, demand for improved fuel efficiency, need to reduce downtime, and advances in technology.  Also, Automotive manufacturers are under immense pressure to meet safety standards and deliver vehicles with low defect rates. FDC systems provide a proactive approach to identifying issues early in the manufacturing process, reducing the likelihood of recalls and safety-related defects. This not only saves manufacturers from costly recalls but also enhances their reputation for producing safe and reliable vehicles.

By offering, the software segment is expected to grow with a highest CAGR in 2028.

Software offering is expected to exhibit a highest CAGR in the fault detection and classification market in 2028. The growth can be attributed as fault detection and classification software offers greater flexibility and adaptability compared to hardware-based solutions. It can be easily updated, modified, or reconfigured to accommodate changes in processes, equipment, or data sources. This flexibility is crucial in industries with rapidly evolving technologies or dynamic operational environments, as it allows organizations to stay agile and responsive to new challenges. Also, Software-based FDC solutions can scale easily to accommodate growing data volumes and expanding operations. As businesses expand, they can simply upgrade their software infrastructure to handle increased data sources and analytical demands.

By hardware, camera segment is expected to grow with a highest CAGR during the forecast period.

Cameras is expected to exhibit a highest CAGR in the fault detection and classification market during the forecast period. The growth of fault detection and classification cameras is driven by their advanced imaging technology, real-time monitoring capabilities, cost efficiency, applicability across industries, regulatory compliance, data analytics potential, and alignment with Industry 4.0 trends. As industries strive for higher product quality, efficiency, and competitiveness, FDC cameras continue to play a pivotal role in meeting these objectives.

FDC in Semiconductor Manufacturing

Fault Detection and Classification (FDC) Market Regional Analysis

In 2028, Asia Pacific is projected to hold the highest CAGR of the overall fault detection and classification market

Fault Detection and Classification (FDC) Market Statistics by Region

To know about the assumptions considered for the study, download the pdf brochure

In 2028, Asia Pacific is projected to account for the largest share of the fault detection and classification market.  Asia pacific has witnessed substantial industrial expansion, particularly in manufacturing, automotive, electronics, and semiconductor industries. As these sectors continue to grow, the need for FDC becomes paramount to maintain product quality, optimize production processes, and ensure operational efficiency.

Top Fault Detection and Classification (FDC) Companies - Key Market Players:

• Keyence Corporation (Japan),
• Cognex Corporation (US),
• KLA Corporation (US),
• Teledyne Technologies (US),
• OMRON Corporation (Japan),
• Microsoft (US),
• Tokyo Electron Limited (Japan),
• Siemens (Germany),
• Amazon Web Services, Inc. (US),
• Synopsys, Inc. (US) are some of the key players in the fault detection and classification companies.

Fault Detection and Classification (FDC) Market Report Scope :

Fault Detection and Classification (FDC) Market Highlights

This research report categorizes the fault detection and classification market based on by fault types, technology, offering, application, end-use, and region.

Recent Developments in Fault Detection and Classification (FDC) Industry

• In August 2023, Synopsys, Inc. launched Synopsys Software Risk Manager, a powerful new application security posture management (ASPM) solution. Software Risk Manager enables security and development teams to simplify, align and streamline their application security testing across projects, teams and application security testing (AST) tools.
• In August 2022, Synopsys, Inc. collaborated with NowSecure (US), experts in mobile security and privacy, and Secure Code Warrior, provider of the leading agile learning platform for developer-driven security, to expand its Software Integrity Group's industry-leading portfolio of application security testing (AST) solutions.
• In July 2022, Microsoft collaborated with Birlasoft to Establish Generative AI Centre of Excellence, Shares Rebound After Announcement. Birlasoft will utilize Azure OpenAI Service features for product design, process optimization, quality and defect detection, predictive maintenance, and digital twins for the manufacturing sector.
• In may 2023, KLA Corporation partnered with imec (Belgium), a nanoelectronics research center. The partnership is to establish the Semiconductor Talent and Automotive Research (STAR) initiative, which is focused developing the talent base and infrastructure necessary to accelerate advanced semiconductor applications for electrification and autonomous mobility and move the automotive industry forward.
• In April 2023, The company launched In-Sight 3800 vision system. designed for high-speed production lines, In-Sight 3800 offers an extensive vision toolset, powerful imaging capabilities, and flexible software to deliver a fully integrated solution for a wide range of inspection applications.

Key Questions Addressed by the Report

To speak to our analyst for a discussion on the above findings, click Speak to Analyst