Fault Detection and Classification Market Size, Share, Statistics and Industry Growth Analysis
Fault Detection and Classification Market Dynamics and Industry Opportunities
The global fault detection and classification market was valued at USD 4.4 billion in 2022 and is projected to reach USD 7.4 billion by 2028; it is expected to register a CAGR of 8.9% between 2023 and 2028
The rise in demand for FDC systems is attributed to the increased complexity of systems, strong focus of manufacturers on automating quality control and quality assurance processes, and stringent health and safety measures imposed by governments and standards organizations on global manufacturing firms.
Fault Detection and Classification Market Dynamics:
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.
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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:
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
The hardware segment accounted for the larger market share in 2028. Hardware offering types in FDC systems include cameras, frame grabbers, optics, and processors. Cameras are the principal hardware of FDC systems. Smart camera-based FDC systems are gaining traction over PC-based systems owing to their easy configuration, validation, and maintenance. There are also continuous advancements in camera technology in terms of resolution, frame rates, imaging technologies, and other parameters to improve the image quality of objects on production lines.
The manufacturing application accounted for the larger market share in 2028. In the realm of manufacturing, faults can manifest in diverse forms, ranging from structural faults and dimensional variations to cosmetic blemishes and functional shortcomings. These issues can have a significant impact on a product's performance, reliability, safety, and aesthetics. Therefore, fault detection is not only about adhering to industry standards but also about upholding brand reputation and customer trust.
Electronics & semiconductors accounted for the larger market share in 2028. Rigorous quality management and visual fault detection are more critical in the electronics industry than for semiconductors. Manual inspection cannot detect various macro and microscopic defects within multiple display pixels, missing components, and small cracks, resulting in quality issues and low productivity. Hence, automatic FDC systems are used to identify macro-defects, which can be found in flat-panel displays (FPDs), such as thin-film-transistor liquid-crystal display (TFT-LCD) glass, plasma display panels (PDPs), and organic light-emitting diodes (OLEDs).
North America held the second largest share of ~32% in the fault detection and classification market in 2022. ?The growth in this region is attributed to the presence of fault detection and classification system manufacturers as Cognex Corporation, Teledyne Technologies, KLA Corporation, Microsoft, etc. Moreover, North America is well known for the early adoption of new technologies that include AI, deep learning, and machine learning. Hence, manufacturers in the region are ready to integrate AI technologies into their processes to increase their production capacity and improve quality. The growing stringency of government regulations pertaining to process standardization and quality assurance across all verticals in North America is responsible for the growth of the fault detection and classification market in North America.
Fault Detection and Classification Industry Key Players :
This research report categorizes the fault detection and classification market based on by fault types, technology, offering, application, end-use, and region.
Subsegment
By Fault Type
Dimensional Fault
Surface Defects
Contamination Faults
Process Variability
Others
领英推荐
Sensor Data Analysis
Statistical Methods
Machine Learning Algorithm
Others
Introduction
Software
Hardware
Cameras
Cameras, by format
Area scan cameras
Line scan cameras
Cameras, by frame rate
Sensors
CCD sensor
CMOS sensors
CMOS sensor
Frame Grabbers
Optics
Processors
Services
Manufacturing
Assembly Verification
Flaw Detection
Fabrication Inspection
Packaging
Grading
Label Validation
Container/Packaging Inspection
Introduction
Automotive
Electronic and semiconductor
Metals & machinery
Food & packaging
Pharmaceuticals