A Fault Detection Method for Lightweight Semi-Trailer Axles

Technical Field

The present invention relates to the technical field of axle fault detection, in particular to a fault detection method for lightweight semi-trailer axles.

Background Art

With the development of modern transportation industry, semi-trailers serve as core equipment for cargo transportation, and their safety and reliability attract growing concerns. As the critical load-bearing and force-transferring component, the operating performance of semi-trailer axles directly determines vehicle driving safety. Conventional axle fault detection relies heavily on manual inspection and empirical judgment, which suffers from low detection efficiency and difficulty in identifying latent faults accurately.

Chinese Patent Publication No. CN113592916A discloses a fault detection method and system for sintering machine trolley axles. The method captures trolley video frames at fixed frame intervals, executes frame difference operation and subsequent image processing, locates wheel regions via deep learning object detection algorithm, and judges trolley wheel swing by comparing pixel gray-scale difference between adjacent frames against preset threshold. Nevertheless, the existing technical solution exhibits prominent drawbacks: low detection accuracy for lightweight semi-trailer axle faults and consequent poor detection efficiency.

Summary of the Invention

To address low detection efficiency of conventional technologies for lightweight semi-trailer axle faults, the present invention provides a lightweight semi-trailer axle fault detection method with the following technical steps:

Step S1: Judge qualification of external operating environment for lightweight semi-trailers according to operating environment evaluation value; evaluate qualification of axle wear degree either by acoustic-vibration evaluation value of rotating axle, or comprehensive evaluation value composed of rotating axle acoustic-vibration frequency and thermal imaging area distribution ratio.

Step S2: If the axle wear degree is unqualified, calculate the first axle wear evaluation value from the ratio of real acoustic-vibration evaluation value to preset acoustic-vibration evaluation value, or calculate the second axle wear evaluation value from the ratio of real comprehensive evaluation value to preset comprehensive evaluation value.

Step S3: Compare the first axle wear evaluation value with the first historical wear evaluation value to verify validity of current wear judgment criteria; adjust the preset acoustic-vibration evaluation threshold based on comparison result between the ratio of current-first historical wear values and preset threshold ratio.

Step S4: Compare the second axle wear evaluation value with the second historical wear evaluation value to verify validity of current wear judgment criteria; adjust the preset comprehensive evaluation threshold based on comparison result between the ratio of current-second historical wear values and preset threshold ratio.

Step S5: Determine the semi-trailer’s actual driving environment via driver driving habit evaluation value, and confirm data acquisition adjustment strategy according to relative deviation between real driving habit evaluation value and historical driving habit evaluation value.

Further Embodiment Restrictions

1. When the operating environment evaluation value ≤ preset operating environment threshold, axle wear is determined unqualified if the acoustic-vibration evaluation value exceeds the preset acoustic-vibration reference value.

2. Under unqualified axle wear condition: current wear judgment standard is invalid when the first wear evaluation value ≤ the first historical wear evaluation value.

3. When wear judgment standard is invalid: raise preset acoustic-vibration reference value by vibration adjustment coefficient if the ratio of first real wear value to first historical wear value ≤ preset ratio threshold.

4. When the operating environment evaluation value ＞ preset operating environment threshold, axle wear is unqualified if the comprehensive evaluation value ≤ preset comprehensive reference value.

5. Under unqualified axle wear condition: current wear judgment standard is invalid when the second wear evaluation value ≤ the second historical wear evaluation value.

6. When wear judgment standard is invalid: raise preset comprehensive reference value by comprehensive adjustment coefficient if the ratio of second real wear value to second historical wear value ≤ preset ratio threshold.

7. When real driving habit evaluation value ≤ historical driving habit evaluation value: increase sampling frequency of acoustic-vibration frequency data if their relative deviation ≤ first preset relative deviation.

8. When real driving habit evaluation value ≤ historical driving habit evaluation value: increase sampling frequency of thermal imaging area distribution data if their relative deviation ＞ first preset relative deviation.

9. When real driving habit evaluation value ＞ historical driving habit evaluation value: increase sampling frequency of acoustic-vibration frequency data if their relative deviation ≤ second preset relative deviation.

Beneficial Effects compared with Prior Art

1. The invention distinguishes stable/rough external driving environment by comparing real and preset environment evaluation values, then assesses axle wear status via acoustic-vibration index under qualified environment, improving axle state identification and overall fault detection efficiency.

2. Historical wear data is introduced to inspect validity of wear evaluation criteria; preset evaluation thresholds are dynamically updated based on ratio between real and historical wear indexes, making judgment benchmarks adapt to practical vehicle operating conditions and boosting evaluation precision and detection efficiency.

3. For poor external operating conditions, the method combines acoustic-vibration frequency and thermal imaging area distribution to build comprehensive evaluation index for axle wear assessment, further improving detection precision and efficiency.

4. When wear evaluation criteria fail validation, the scheme revises preset comprehensive reference value via real-to-historical wear ratio comparison to realize accurate wear qualification discrimination.

5. Driving habit evaluation value reflects actual road condition; data collection strategy is optimized according to deviation between real and historical driving habits to achieve refined vehicle condition monitoring and improve axle fault detection accuracy.

6. Refined adjustment of acoustic-vibration and thermal imaging sampling frequency based on driving habit variation facilitates early discovery of hidden axle defects and promotes detection precision and efficiency.

7. Under stable driving environment, dynamic tuning of acoustic-vibration sampling frequency according to driving habit fluctuation realizes precise tracking of vehicle performance variation and optimizes lightweight semi-trailer axle fault detection efficiency.

Brief Description of the Drawings

FIG.1: Flowchart of the lightweight semi-trailer axle fault detection method according to the embodiment of the invention;
FIG.2: Flowchart for external operating environment qualification judgment;
FIG.3: Flowchart for axle wear degree qualification judgment;
FIG.4: Flowchart for validity verification of real-time wear evaluation criteria.

Detailed Description of Preferred Embodiments

The invention is further elaborated with preferred embodiments and attached drawings for clear demonstration of objectives and advantages. The illustrated embodiments serve only for descriptive purpose rather than limitation of the present invention.

Terms such as upper, lower, left, right, inner and outer in this specification refer to positional relationships shown in attached drawings only for convenient description, instead of mandatory fixed layout of related components or devices.

Terms including mount, connect and link shall be interpreted in broad sense: fixed connection, detachable connection or integral forming; mechanical connection or electrical connection; direct coupling or indirect connection via intermediate medium as applicable, which can be defined by those skilled in the art under specific practical scenarios.

Detailed Implementation of Each Step

Step S1: Environment and Axle Wear Qualification Judgment

The preset reference value of operating environment evaluation is set as 0.86, calculated by averaging multiple historical qualified environment evaluation data and adjustable per practical application demand.

• Operating environment evaluation value E ≤ 0.86 → external environment qualified (smooth pavement);

• Operating environment evaluation value E ＞ 0.86 → external environment unqualified (rough/bumpy pavement).

Under qualified external environment, preset acoustic-vibration reference value = 0.9:

• Acoustic-vibration evaluation value P ≤ 0.9 → axle wear qualified;

• Acoustic-vibration evaluation value P ＞ 0.9 → axle wear unqualified.

Under unqualified external environment, preset comprehensive evaluation reference value = 0.85:

• Comprehensive evaluation value Q ≤ 0.85 → axle wear unqualified;

• Comprehensive evaluation value Q ＞ 0.85 → axle wear qualified.

Step S2 & S3: First Wear Evaluation and Vibration Threshold Dynamic Adjustment

First historical wear evaluation benchmark is set as 0.89, preset comparison ratio threshold = 0.74.
If first real wear value ≤ 0.89 → real-time evaluation standard invalid;
When invalid standard confirmed:

• Real/historical wear ratio ≤0.74 → update preset vibration reference:  (: original preset vibration value; : vibration adjustment coefficient);

• Real/historical wear ratio ＞0.74 → keep original preset vibration value unchanged.
 is calculated from deviation between real ratio and preset threshold ratio.

Step S4: Second Wear Evaluation and Comprehensive Threshold Dynamic Adjustment

Second historical wear evaluation benchmark = 0.82, preset comparison ratio threshold = 0.7.
If second real wear value ≤0.82 → real-time evaluation standard invalid;
When invalid standard confirmed:

• Real/historical wear ratio ≤0.7 → update preset comprehensive reference:  (: original preset comprehensive value; : comprehensive adjustment coefficient);

• Real/historical wear ratio ＞0.7 → keep original preset comprehensive value unchanged.
 is calculated from deviation between real ratio and preset threshold ratio.

Step S5: Sampling Frequency Adjustment Based on Driving Habit Evaluation

Historical driving habit evaluation benchmark = 0.78.

• Real driving habit value B ≤0.78: vehicle runs on uphill/downhill or bumpy unsmooth road; first preset relative deviation = 0.2

￮ Relative deviation ≤0.2: raise acoustic-vibration sampling frequency; adjust by coefficient 1.03 (deviation ≤0.1) or 1.07 (deviation＞0.1), ;

￮ Relative deviation＞0.2: raise thermal imaging sampling frequency; adjust by coefficient 1.05 (deviation ≤0.07) or 1.09 (deviation＞0.07), .

• Real driving habit value B＞0.78: vehicle runs on smooth road; second preset relative deviation =0.25, third preset difference=0.15

￮ Relative deviation ≤0.25: increase acoustic-vibration sampling frequency with coefficient 1.04(deviation ≤0.15) or 1.08(deviation＞0.15), ;

￮ Relative deviation＞0.25: maintain original sampling frequency.

Closing Statement of Rights

Although the technical solution of the invention is described in combination with preferred embodiments and attached drawings, the protection scope is not limited to these specific embodiments. Equivalent modification or replacement on technical features without departing from the core principle of the invention shall all fall into the protection scope of the present invention.

The above embodiments are preferred embodiments of the invention and shall not restrict the protection scope. Any amendment, equivalent replacement and improvement made within the spirit and principle of the invention are covered by its protection scope.