NDT 2025 , 3 , 7
2of 12
including nuclear and renewable energy, NDT is employed to assess turbines and high- pressure systems [5]. However, these conventional NDT techniques often lead to higher costs and increased system complexity. While effective, these methods require additional infrastructure and regular inspections, frequently hindering continuous data capture. With the growth of industrial automation and predictive maintenance strategies, diag- nostic data from variable frequency drives (VFDs) have become a valuable yet underutilized resource in NDT. Variable frequency drives are designed to monitor critical motor and apparatus parameters internally and online for self-protection and enhanced performance, thus serving as an inbuilt repository for external data [6]. When VFD data are exported and utilized even for simple condition monitoring, they can provide real-time, continuous surveillance of system health with minimal reliance on external sensors, reduced costs, and decreased complexity [7]. This technical note addresses the underutilized role of variable frequency drives (VFDs) in enhancing nondestructive testing (NDT) and diagnostics to improve the reliability and safety of machinery, materials, and components. Rather than relying on only traditional NDT methods that often require periodic checks and manual application, VFDs provide real-time operational data to help detect anomalies during operation [2,8]. Furthermore, VFD data can be integrated into machine learning (ML) and artificial intelligence (AI) models to predict failures and optimize maintenance strategies using archived data [6,9]. Importantly, VFD-based diagnostics do not aim to replace conventional NDT methods but rather serve as a complementary tool to enhance current data acquisition and support NDT efforts [10]. By combining the information obtained from VFDs with traditional NDT techniques, users can shift toward more predictive, data-driven approaches and maximize the advantages of NDT applied across industries. 2. VFD Data for NDT Applications The primary function of a VFD is to control the speed of electric induction motors (or variations of permanent magnetic motors) across their entire operating range [11]. In its simplest form, a VFD manages three key output characteristics: the fundamental frequency and voltage magnitude, which maintain a fixed ratio to produce torque in the motor, and the carrier frequency, which determines the quality of switching operations, typically at a minimum of 2 kHz [11]. Digital processing capabilities have been integrated into modern VFD designs since their inception, allowing precise motor control as well as the collection and storage of diagnostic data [7,11]. These data facilitate the monitoring of the drive itself, the power supply, the electric motor, and even the attached load [7]. While very few parameters of early VFDs were available for export for reliability analysis and NDT, modern designs offer most of these parameters for the user’s benefit [7,11,12]. One of the most compelling motivations for accessing VFD data lies in their potential for sensorless condition monitoring, particularly for critical industrial equipment such as centrifugal pumps [5]. Pumps are essential in various industries, including pulp and paper production, wastewater treatment, and chemical processing, where reliability and energy efficiency are paramount [5]. As noted, traditional condition monitoring systems rely on external sensors, which increase system complexity and cost [12]. Recent research has demonstrated that motor torque estimation data can be utilized for fault detection, eliminating the need for external sensors and underscoring the flexible and valuable data generated by VFDs [5]. However, a significant challenge in condition monitoring is the scarcity of data indicating a fault or a defective condition requiring action. The literature often highlights deficiencies similar to those of the critical pumps detailed by Turunen et al. [5]. The answer to this data imbalance may potentially be found in the AI and ML techniques discussed later.
Made with FlippingBook interactive PDF creator