Pawel Rzeszucinski

Stator circulating currents as media of fault detection in synchronous motors

Often found in critical, high power applications, synchronous machines require reliable condition monitoring systems. Large synchronous machines are typically designed with parallel connected windings in order to split the currents in parallel paths, delivering the total power at the terminals. Under ideal symmetrical conditions, no current will circulate between parallel branches of the same phase. However, when a motor fault breaks this symmetry, currents circulate between the branches. Thus, due to the fact that they are only non-zero under faulty conditions, circulating currents potentially represent a sensitive indicator of faulty condition. In this paper, the advantages of using the circulating current between parallel branches of the stator of a synchronous motor as an early indicator of motor faults are shown. Analysis is conducted both through simulation, via the use of finite element methods (FEM), and through experimentation using a specially-designed synchronous machine which allows various fault conditions to be investigated. Through comparison between experiment and simulation, the simulation tool is validated. Furthermore, it is shown that the circulating current is better suited for fault detection than either the branch or the stator current. It is concluded that an improved condition monitoring and protection system for a synchronous machine may be achieved if these currents are monitored.

A signal processing approach to bearing fault detection with the use of a mobile phone

According to statistics, bearings are the most often failing elements of low voltage motors. At the same time diagnostics of rolling element bearings constitutes a well-established part of the rotating machinery condition monitoring domain. In many cases however the cost of installing a high-end accelerometer based bearing condition monitoring system, which is currently the most common approach in the industry, might be difficult to justify on non-critical machinery due to potentially long payback period on the investment. This text investigates the possibility of performing condition monitoring of rolling element bearings based on acoustic signals recorded by a standard, easily accessible mobile phone. The main difficulty in using mobile phone-embedded microphone for rotating machinery diagnostic purposes is the fact that the frequency response of the mobile phone microphone is very poor below 200Hz. The results presented in this text seem to indicate that with an appropriate signal processing approach, it is possible to indicate the presence of faults in the bearings.

Condition monitoring of electric motors based on magnetometer measurements

Using micro-sensors in industrial applications is of great interest due to their small size, low-cost and little power consumption. However, the harsh environmental conditions encountered in an industrial environment have so far hindered the widespread use of, for example, MEMS-based sensors. Such sensors are particularly suited for mobile condition monitoring of industrial machinery as short time placement and operation of these sensors is typically unproblematic for many monitoring applications. In this paper we use a miniature triaxial geomagnetic sensor for condition monitoring of low voltage motors. The performance of the magnetometer is studied under conditions encountered in industry. Furthermore the magnetometer is used to measure magnetic fields of an electric motor in a healthy state and in case of a broken rotor bar. By frequency analysis of this data it is demonstrated that the magnetometer measurements can be employed to distinguish between these motor conditions.

Bearing fault detection with the use of acoustic signals recorded by a hand-held mobile phone

It is well know that bearings are the most often failing elements of electric motors. Percentage of bearing failures vs. other failures is even more significant for the case of low voltage motors. At the same time diagnostics of rolling element bearings constitutes a well-established part of the rotating machinery condition monitoring domain. In many cases, however, the cost of installing a high-end accelerometer-based bearing condition monitoring system, which is currently the most common approach in the industry, might be difficult to justify on non-critical machinery due to potentially long payback period of the investment. Therefore, still the very first diagnostic is often performed by human ear and the assessment of the nature of the emitted sound. Considering an example of electric motors, it is typically the abnormal or excessive sound that is first recognized by the operators as the indicator of faulty motor operating condition. This text investigates the possibility of performing condition monitoring of rolling element bearings based on acoustic signals recorded by a standard and easily accessible mobile phone. The main difficulty in using mobile phone-embedded microphone for rotating machinery diagnostic purposes is the fact that the frequency response of the mobile phone microphone is very poor (below 200 Hz) where typically the bearing fault frequencies lie. The results seem to indicate that given an appropriate signal processing approach is taken, it is possible to indicate the presence of faults in the bearings.

Ensemble Empirical Mode Decomposition and Sparsity Measurement as Tools Enhancing the Gear Diagnostic Capabilities of Time Synchronous Averaging

The paper introduces the concept of exploring the potential of Ensemble Empirical Mode Decomposition (EEMD) and Sparsity Measurement (SM) in enhancing the diagnostic information contained in the Time Synchronous Averaging (TSA) method used in the field of gearbox diagnostics. EEMD was created as a natural improvement of the Empirical Mode Decomposition which suffered from a so-called mode mixing problem. SM is heavily used in the field of ultrasound signal processing as a tool for assessing the degree of sparsity of a signal. A novel process of automatically finding the optimal parameters of EEMD is proposed by incorporating a Form Factor parameter, known from the field of electrical engineering. All these elements are combined and applied on a set of vibration data generated on a 2-stage gearbox under healthy and faulty conditions. The results suggest that combining these methods may increase the robustness of the condition monitoring routine, when compared to the standard TSA used alone.

A Frequency-Based Criterion for Automatic Selection of the Optimal Intrinsic Mode Function in Diagnostics of Localized Gear Tooth Faults

To date gearboxes remain one of the most important elements of virtually every power transmission system as far as a continuous operation of the shaft line is concerned. Any failure or breakdown may result in putting the whole production line, supply chain or even peoples life in jeopardy. Endeavours to detect an incipient fault within the system serve multiple purposes from increasing the safety of the people responsible for operating the machines, through to decreasing running and operational costs, allowing time to plan for the inevitable repairs and making sure that the downtime of the machine is kept to an absolute minimum. This, in turn, makes this branch of condition monitoring of rotating machinery one of the most intensively studied. The Empirical Mode Decomposition (EMD) is a relatively new method of signal decomposition, which breaks the original signal up into a number of so-called Intrinsic Mode Functions (IMFs). The decomposition represents a type of adaptive filtering which outputs a number of IMFs which, acquired according to two strict criteria, contain portions of the filtered version of the original signal and so can carry different information about the content of the signal. EMD has already been used in the field of condition monitoring of rotating machinery, but the selection of the optimal IMF for the task often requires the experience of a condition monitoring specialist. This paper proposes a frequency-based tool for automatic selection of the IMF that is best suited for the detection of localized gear tooth faults.

Low cost, hand held acoustic camera

The work described in this paper focuses on the design of a low cost, hand held acoustic camera. The novel process of creating the camera has been entirely engineered in ABB Ltd. Even though the creation of the acoustic camera with the use of 3D printing techniques may come at the cost of slightly decreased accuracy of the measurements, when compared with the commercially available solutions, however this is entirely acceptable in many applications. Additionally the costs saving related with the in-house manufacturing adds to the attractiveness of such an approach. The paper aims to present that with the advent of new technologies e.g. 3D printing it becomes possible for simple-design tools capable of delivering high quality results to be created by virtually anyone.

Condition monitoring of synchronous motors based on measurement of circulating stator currents

Condition monitoring based on currents circulating between parallel stator windings of a synchronous motor has been demonstrated. Circulating currents arise from any asymmetries in the motor that influence the air-gap magnetic field such as an eccentric rotor or an inter-turn short circuit. A measurement system based on six individual current sensors has been designed to acquire current data on an industrial motor with high fidelity in order to get a better understanding of the sensitivity of the circulating currents to the different motor states. The selection of appropriate sensors as well as their dielectric layout is discussed in detail. The frequency analysis of the acquired current waveforms yields a rich and complex spectrum, proving the very high sensitivity of circulating currents even to small motor asymmetries.

Parallel Autoregressive Modeling as a Tool for Diagnosing Localized Gear Tooth Faults

One of the standard approaches widely used in the field of localized gear tooth fault diagnosis is the creation of residual signals i.e. signals obtained after removing the deterministic frequency components from a Time Synchronously Averaged vibration signals. Most of the time these components are removed based on the knowledge of the characteristic gearbox frequencies. Sometimes however such information is not available. AR modeling, a type of time series modeling, has been found to be capable of faithfully estimating the deterministic content of the signal allowing meaningful residual signals to be created. An improvement to the classic AR modeling approach is proposed in this text. The method is applied to experimental data taken from a gearbox in both healthy and faulty condition. The improvement derived from the new method is quantified through a comparison with results obtained by applying Time Synchronous Averaging and the classic AR modeling method to the experimental data.